Ford: add measured-curvature feedback to C1

Add a distance-integrated steering-curvature correction to the restored v1 heading request. Hold the correction at zero error, allow unwind at limits, and clear it for driver override or inactive PSCM control. Preserve C0, C2=C3=0, final output limits, the 100 Hz sender and the existing opt-in toggle. Validate build/hold/unwind, measurement cadence, anti-windup and the actual controlsd-to-CAN path. The combined suite passes 511 tests and 9,146 subtests; 178 inherited or unsupported safety variants skip. Stress and frozen b8 replay pass 578,569 Float32/CAN round trips. These checks do not establish physical tracking or stability. Record reproduction steps and source hashes.
Ford: restore original selected-curvature v1 controller
2026-09-10 02:43:42 +08:00 · 2026-09-09 10:46:41 -04:00 · 2026-09-08 21:44:44 -04:00 · 2026-09-08 20:37:34 -04:00 · 2026-09-08 16:37:58 -04:00 · 2026-09-08 13:53:15 -04:00
256 changed files with 12047 additions and 3194 deletions
@@ -9,6 +9,7 @@
 *.ttf filter=lfs diff=lfs merge=lfs -text
 *.otf filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+openpilot/selfdrive/assets/sounds/milestone.wav -filter -diff -merge -text

 openpilot/selfdrive/car/tests/test_models_segs.txt filter=lfs diff=lfs merge=lfs -text
 openpilot/common/hardware/comma/updater filter=lfs diff=lfs merge=lfs -text
@@ -1,11 +0,0 @@
-*       @sunnypilot/dev-internal
-/.github/ @devtekve @sunnyhaibin
-/release/ci/ @devtekve @sunnyhaibin
-/tinygrad_repo @devtekve @Discountchubbs
-/tinygrad/ @devtekve @Discountchubbs
-/selfdrive/controls/lib/longitudinal_planner.py @devtekve @Discountchubbs
-/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py @devtekve @Discountchubbs
-/selfdrive/modeld/ @devtekve @Discountchubbs
-/sunnypilot/model* @devtekve @Discountchubbs
-/sunnypilot/sunnylink/ @devtekve
-/system/athena/ @devtekve
@@ -8,13 +8,13 @@ on:
        required: true
        type: string
      target_hardware:
-        description: 'Hardware target to compile for (qcom or usbgpu)'
+        description: 'Hardware target to compile for (qcom or chestnut)'
        required: true
        type: choice
        default: 'qcom'
        options:
          - qcom
-          - usbgpu
+          - chestnut
      hf_repo:
        description: 'Hugging Face dataset repository'
        required: false
@@ -59,7 +59,7 @@ jobs:
        id: get-json
        run: |
          cd docs/docs
-          PREFIX="driving_models_${{ inputs.target_hardware == 'usbgpu' && 'usbgpu_' || '' }}v"
+          PREFIX="driving_models_${{ inputs.target_hardware == 'chestnut' && 'chestnut_' || '' }}v"
          latest=$(ls ${PREFIX}*.json | sed -E "s/${PREFIX}([0-9]+)\.json/\1/" | sort -n | tail -1)
          next=$((latest+1))
          json_file="${PREFIX}${next}.json"
@@ -78,6 +78,7 @@ jobs:
      - name: Get next recompiled dir number
        id: create-recompiled-dir
        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
          HF_REPO: ${{ github.event.inputs.hf_repo }}
        run: |
          pip install huggingface_hub
@@ -1,83 +0,0 @@
-name: Build default big model
-
-on:
-  workflow_dispatch:
-
-env:
-  HF_REPO: sunnypilot/sunnypilot_models_v1
-  HF_DEFAULTS_PATH: models/defaults/big
-
-jobs:
-  resolve_name:
-    runs-on: ubuntu-24.04
-    outputs:
-      model_name: ${{ steps.name.outputs.model_name }}
-      onnx_ref: ${{ steps.name.outputs.onnx_ref }}
-    steps:
-      - uses: actions/checkout@v4
-      - id: name
-        run: |
-          NAME=$(PYTHONPATH=${{ github.workspace }} python3 -c "from openpilot.sunnypilot.models.model_name import DEFAULT_BIG_MODEL; print(DEFAULT_BIG_MODEL)")
-          ONNX_REF=$(git log -1 --format='%H' -- openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx)
-          echo "model_name=${NAME}" >> $GITHUB_OUTPUT
-          echo "onnx_ref=$ONNX_REF" >> $GITHUB_OUTPUT
-
-  build_model:
-    needs: resolve_name
-    uses: ./.github/workflows/sunnypilot-build-model.yaml
-    with:
-      upstream_branch: ${{ needs.resolve_name.outputs.onnx_ref }}
-      custom_name: ${{ needs.resolve_name.outputs.model_name }}
-      target_hardware: usbgpu
-    secrets: inherit
-
-  upload_defaults:
-    needs: [ resolve_name, build_model ]
-    runs-on: ubuntu-24.04
-    permissions:
-      id-token: write
-      contents: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - run: git lfs pull -I "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
-
-      - name: Install huggingface_hub
-        run: pip install --upgrade "huggingface_hub>=0.22.0"
-
-      - name: Download artifact name
-        uses: actions/download-artifact@v4
-        with:
-          name: artifact-name-${{ needs.resolve_name.outputs.model_name }}
-          path: artifact_name
-
-      - name: Read artifact name
-        id: artifact
-        run: |
-          ARTIFACT_NAME=$(cat artifact_name/artifact_name.txt)
-          echo "artifact_name=$ARTIFACT_NAME" >> $GITHUB_OUTPUT
-
-      - name: Download model artifact
-        uses: actions/download-artifact@v4
-        with:
-          name: ${{ steps.artifact.outputs.artifact_name }}
-          path: output
-
-      - name: Upload to HF and update default_models.json
-        env:
-          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
-          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
-        run: |
-          rm -f output/artifact_name.txt
-          export PYTHONPATH=$(pwd)
-          python3 release/ci/upload_default_model.py \
-            --hf-repo "${{ env.HF_REPO }}" \
-            --hf-defaults-path "${{ env.HF_DEFAULTS_PATH }}" \
-            --artifact-name "$ARTIFACT_NAME" \
-            --model-dir output \
-            --onnx-path "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx" \
-            --onnx-ref "${{ needs.resolve_name.outputs.onnx_ref }}" \
-            --model-name "${{ needs.resolve_name.outputs.model_name }}" \
-            --tinygrad-ref "$(python3 openpilot/sunnypilot/models/tinygrad_ref.py)" \
-            --run-number "${{ github.run_number }}"
@@ -0,0 +1,522 @@
+name: Build default models
+
+on:
+  workflow_dispatch:
+    inputs:
+      target:
+        description: 'Model target to build'
+        required: true
+        type: choice
+        options:
+          - small
+          - big
+          - dm
+  workflow_call:
+    inputs:
+      target:
+        description: 'Model target to build (small, big, or dm)'
+        required: true
+        type: string
+
+concurrency:
+  group: build-default-models-${{ inputs.target }}
+  cancel-in-progress: false
+
+env:
+  HF_REPO: sunnypilot/sunnypilot_models_v1
+
+jobs:
+  resolve:
+    runs-on: ubuntu-24.04
+    outputs:
+      model_name: ${{ steps.resolve.outputs.model_name }}
+      safe_model_name: ${{ steps.resolve.outputs.safe_model_name }}
+      onnx_ref: ${{ steps.resolve.outputs.onnx_ref }}
+      onnx_path: ${{ steps.resolve.outputs.onnx_path }}
+      hf_defaults_path: ${{ steps.resolve.outputs.hf_defaults_path }}
+      tinygrad_ref: ${{ steps.resolve.outputs.tinygrad_ref }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - id: resolve
+        run: |
+          export PYTHONPATH=${{ github.workspace }}
+
+          if [ "${{ inputs.target }}" = "big" ]; then
+            NAME=$(python3 -c "from openpilot.sunnypilot.models.model_name import DEFAULT_BIG_MODEL; print(DEFAULT_BIG_MODEL)")
+            ONNX_PATH="openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
+            HF_DEFAULTS_PATH="models/defaults/big"
+          elif [ "${{ inputs.target }}" = "dm" ]; then
+            ONNX_PATH="openpilot/selfdrive/modeld/models/dmonitoring_model.onnx"
+            HF_DEFAULTS_PATH="models/defaults/dm"
+            NAME="dmonitoring_model ($(git log -1 --format=%cd --date=format:'%B %d, %Y' -- "$ONNX_PATH"))"
+          else
+            NAME=$(python3 -c "from openpilot.sunnypilot.models.model_name import DEFAULT_MODEL; print(DEFAULT_MODEL)")
+            ONNX_PATH="openpilot/selfdrive/modeld/models/driving_supercombo.onnx"
+            HF_DEFAULTS_PATH="models/defaults/small"
+          fi
+
+          ONNX_REF=$(git log -1 --format='%H' -- "$ONNX_PATH")
+          TINYGRAD_REF=$(python3 openpilot/sunnypilot/models/tinygrad_ref.py)
+          if [ -z "$TINYGRAD_REF" ]; then
+            echo "::error::Failed to resolve tinygrad ref"
+            exit 1
+          fi
+
+          SAFE_NAME="${NAME// /-}"
+          echo "model_name=${NAME}" >> $GITHUB_OUTPUT
+          echo "safe_model_name=${SAFE_NAME}" >> $GITHUB_OUTPUT
+          echo "onnx_ref=${ONNX_REF}" >> $GITHUB_OUTPUT
+          echo "onnx_path=${ONNX_PATH}" >> $GITHUB_OUTPUT
+          echo "hf_defaults_path=${HF_DEFAULTS_PATH}" >> $GITHUB_OUTPUT
+          echo "tinygrad_ref=${TINYGRAD_REF}" >> $GITHUB_OUTPUT
+
+  build_small_model:
+    needs: resolve
+    if: ${{ inputs.target == 'small' }}
+    runs-on: [self-hosted, tici]
+    env:
+      SMALL_ONNX: openpilot/selfdrive/modeld/models/driving_supercombo.onnx
+      SMALL_PKL: openpilot/selfdrive/modeld/models/driving_tinygrad.pkl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - name: Pull ONNX via LFS
+        run: git lfs pull -I "${{ env.SMALL_ONNX }}"
+
+      - name: Set environment variables
+        run: |
+          source /etc/profile
+          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
+          export UV_PYTHON_PREFERENCE=managed
+          export UV_PYTHON_INSTALL_DIR=${HOME}/uv/python
+          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
+          uv sync --frozen
+          printenv >> $GITHUB_ENV
+
+      - name: Disable powersave
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable
+
+      - name: Compile small model with stock compiler
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+
+          MODEL_SIZE=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
+          CAMERA_RES=$(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')")
+          FRAME_SKIP=$(python3 -c "from openpilot.selfdrive.modeld.constants import ModelConstants as MC; print(MC.MODEL_RUN_FREQ // MC.MODEL_CONTEXT_FREQ)")
+
+          TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+
+          env ${TG_FLAGS} python3 \
+            ${{ github.workspace }}/openpilot/selfdrive/modeld/compile_modeld.py \
+            --onnx ${{ github.workspace }}/${{ env.SMALL_ONNX }} \
+            --model-size $MODEL_SIZE \
+            --camera-resolutions $CAMERA_RES \
+            --frame-skip $FRAME_SKIP \
+            --output ${{ github.workspace }}/${{ env.SMALL_PKL }}
+
+      - name: Chunk small pkl
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          python3 -c "
+          from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+          import os
+          pkl = '${{ github.workspace }}/${{ env.SMALL_PKL }}'
+          size = os.path.getsize(pkl)
+          targets = get_chunk_targets(pkl, size)
+          chunk_file(pkl, targets)
+          print(f'Chunked into {len(targets)} files')
+          "
+
+      - name: Prepare output
+        env:
+          MODEL_NAME: ${{ needs.resolve.outputs.safe_model_name }}
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          MODELS_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld/models"
+          OUTPUT_DIR="${{ github.workspace }}/small_output"
+          PKL_BASE="driving_tinygrad.pkl"
+          mkdir -p "$OUTPUT_DIR"
+
+          cp "$MODELS_DIR/${PKL_BASE}".chunk* "$OUTPUT_DIR/"
+          cp "$MODELS_DIR/${PKL_BASE}.chunkmanifest" "$OUTPUT_DIR/"
+
+          python3 "${{ github.workspace }}/release/ci/model_generator.py" \
+            --model-dir "$MODELS_DIR" \
+            --output-dir "$OUTPUT_DIR" \
+            --custom-name "$MODEL_NAME" \
+            --upstream-branch "${{ needs.resolve.outputs.onnx_ref }}"
+
+          echo "model-${MODEL_NAME}-${{ github.run_number }}" > "$OUTPUT_DIR/artifact_name.txt"
+
+      - name: Upload small model artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: model-${{ needs.resolve.outputs.safe_model_name }}-${{ github.run_number }}
+          path: ${{ github.workspace }}/small_output/
+
+      - name: Upload artifact name file
+        uses: actions/upload-artifact@v4
+        with:
+          name: artifact-name-${{ needs.resolve.outputs.safe_model_name }}
+          path: ${{ github.workspace }}/small_output/artifact_name.txt
+
+      - name: Re-enable powersave
+        if: always()
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
+
+  build_big_model:
+    needs: resolve
+    if: ${{ inputs.target == 'big' }}
+    runs-on: [self-hosted, chestnut]
+    env:
+      BIG_ONNX: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
+      BIG_PKL: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - name: Pull big ONNX via LFS
+        run: git lfs pull -I "${{ env.BIG_ONNX }}"
+
+      - name: Set environment variables
+        run: |
+          source /etc/profile
+          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
+          export UV_PYTHON_PREFERENCE=managed
+          export UV_PYTHON_INSTALL_DIR=${HOME}/uv/python
+          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
+          uv sync --frozen
+          printenv >> $GITHUB_ENV
+
+      - name: Disable powersave
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable
+
+      - name: Wait for chestnut PCIe link
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+          python3 -c "
+          import time
+          from openpilot.system.hardware.chestnut.flash import link_up
+          for i in range(10):
+              if link_up():
+                  print(f'PCIe link up after {i+1} attempt(s)')
+                  break
+              time.sleep(1)
+          else:
+              raise RuntimeError('Chestnut PCIe link not ready after 10 attempts')
+          "
+
+      - name: Compile big model with stock compiler
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+
+          MODEL_SIZE=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
+          CAMERA_RES=$(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')")
+          FRAME_SKIP=$(python3 -c "from openpilot.selfdrive.modeld.constants import ModelConstants as MC; print(MC.MODEL_RUN_FREQ // MC.MODEL_CONTEXT_FREQ)")
+
+          TG_FLAGS="DEBUG=2 DEV=USB+AMD:LLVM WARP_DEV=QCOM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2"
+
+          env ${TG_FLAGS} python3 \
+            ${{ github.workspace }}/openpilot/selfdrive/modeld/compile_modeld.py \
+            --onnx ${{ github.workspace }}/${{ env.BIG_ONNX }} \
+            --model-size $MODEL_SIZE \
+            --camera-resolutions $CAMERA_RES \
+            --frame-skip $FRAME_SKIP \
+            --output ${{ github.workspace }}/${{ env.BIG_PKL }}
+
+      - name: Chunk big pkl
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          python3 -c "
+          from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+          import os
+          pkl = '${{ github.workspace }}/${{ env.BIG_PKL }}'
+          size = os.path.getsize(pkl)
+          targets = get_chunk_targets(pkl, size)
+          chunk_file(pkl, targets)
+          print(f'Chunked into {len(targets)} files')
+          "
+
+      - name: Prepare output
+        env:
+          MODEL_NAME: ${{ needs.resolve.outputs.safe_model_name }}
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          MODELS_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld/models"
+          OUTPUT_DIR="${{ github.workspace }}/big_output"
+          PKL_BASE="big_driving_tinygrad.pkl"
+          mkdir -p "$OUTPUT_DIR"
+
+          cp "$MODELS_DIR/${PKL_BASE}".chunk* "$OUTPUT_DIR/"
+          cp "$MODELS_DIR/${PKL_BASE}.chunkmanifest" "$OUTPUT_DIR/"
+
+          python3 "${{ github.workspace }}/release/ci/model_generator.py" \
+            --model-dir "$MODELS_DIR" \
+            --output-dir "$OUTPUT_DIR" \
+            --custom-name "$MODEL_NAME" \
+            --upstream-branch "${{ needs.resolve.outputs.onnx_ref }}"
+
+          echo "model-${MODEL_NAME}-${{ github.run_number }}" > "$OUTPUT_DIR/artifact_name.txt"
+
+      - name: Upload big model artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: model-${{ needs.resolve.outputs.safe_model_name }}-${{ github.run_number }}
+          path: ${{ github.workspace }}/big_output/
+
+      - name: Upload artifact name file
+        uses: actions/upload-artifact@v4
+        with:
+          name: artifact-name-${{ needs.resolve.outputs.safe_model_name }}
+          path: ${{ github.workspace }}/big_output/artifact_name.txt
+
+      - name: Re-enable powersave
+        if: always()
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
+
+  upload_defaults:
+    needs: [ resolve, build_small_model, build_big_model, build_dm_model ]
+    if: |
+      ${{
+        !cancelled() &&
+        (inputs.target == 'big' && needs.build_big_model.result == 'success' ||
+         inputs.target == 'small' && needs.build_small_model.result == 'success' ||
+         inputs.target == 'dm' && needs.build_dm_model.result == 'success')
+      }}
+    runs-on: ubuntu-24.04
+    permissions:
+      id-token: write
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Pull ONNX via LFS
+        run: git lfs pull -I "${{ needs.resolve.outputs.onnx_path }}"
+
+      - name: Install huggingface_hub
+        run: pip install --upgrade "huggingface_hub>=0.22.0"
+
+      - name: Download artifact name
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        uses: actions/download-artifact@v4
+        with:
+          name: artifact-name-${{ needs.resolve.outputs.safe_model_name }}
+          path: artifact_name
+
+      - name: Read artifact name
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        id: artifact
+        run: |
+          ARTIFACT_NAME=$(cat artifact_name/artifact_name.txt)
+          echo "artifact_name=$ARTIFACT_NAME" >> $GITHUB_OUTPUT
+
+      - name: Download model artifact
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        uses: actions/download-artifact@v4
+        with:
+          name: ${{ steps.artifact.outputs.artifact_name }}
+          path: output
+
+      - name: Upload model to HF
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
+        run: |
+          rm -f output/artifact_name.txt
+          export PYTHONPATH=$(pwd)
+          python3 release/ci/upload_default_model.py \
+            --hf-repo "${{ env.HF_REPO }}" \
+            --hf-defaults-path "${{ needs.resolve.outputs.hf_defaults_path }}" \
+            --artifact-name "$ARTIFACT_NAME" \
+            --model-dir output \
+            --onnx-path "${{ needs.resolve.outputs.onnx_path }}" \
+            --onnx-ref "${{ needs.resolve.outputs.onnx_ref }}" \
+            --model-name "${{ needs.resolve.outputs.model_name }}" \
+            --tinygrad-ref "${{ needs.resolve.outputs.tinygrad_ref }}" \
+            --run-number "${{ github.run_number }}"
+
+      - name: Download DM artifact
+        if: ${{ inputs.target == 'dm' }}
+        uses: actions/download-artifact@v4
+        with:
+          name: dm-model-${{ github.run_number }}
+          path: dm_output
+
+      - name: Generate DM metadata and upload to HF
+        if: ${{ inputs.target == 'dm' }}
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          export PYTHONPATH=$(pwd)
+          python3 -c "
+          import json, hashlib
+          from pathlib import Path
+          from datetime import datetime, UTC
+
+          dm_dir = Path('dm_output')
+          manifest = list(dm_dir.glob('*.chunkmanifest'))
+          assert manifest, 'No chunkmanifest found'
+          pkl_name = manifest[0].name.removesuffix('.chunkmanifest')
+          num_chunks = int(manifest[0].read_text().strip())
+
+          chunks = []
+          for i in range(num_chunks):
+              chunk = dm_dir / f'{pkl_name}.chunk{i+1:02d}of{num_chunks:02d}'
+              chunks.append({
+                  'file_name': chunk.name,
+                  'sha256': hashlib.sha256(chunk.read_bytes()).hexdigest()
+              })
+
+          digest = hashlib.sha256()
+          for c in chunks:
+              with open(dm_dir / c['file_name'], 'rb') as f:
+                  while block := f.read(1024*1024):
+                      digest.update(block)
+
+          metadata = {
+              'bundles': [{
+                  'short_name': 'DMMODEL',
+                  'display_name': '${{ needs.resolve.outputs.model_name }}',
+                  'ref': '${{ needs.resolve.outputs.onnx_ref }}',
+                  'runner': 'tinygrad',
+                  'build_time': datetime.now(UTC).strftime('%Y-%m-%dT%H:%M:%SZ'),
+                  'models': [{
+                      'type': 'chunked',
+                      'artifact': {
+                          'file_name': pkl_name,
+                          'download_uri': {'url': '', 'sha256': digest.hexdigest()},
+                          'chunks': chunks
+                      }
+                  }]
+              }]
+          }
+          with open(dm_dir / 'metadata.json', 'w') as f:
+              json.dump(metadata, f, indent=2)
+          print('Generated DM metadata.json')
+          "
+
+          python3 release/ci/upload_default_model.py \
+            --hf-repo "${{ env.HF_REPO }}" \
+            --hf-defaults-path "${{ needs.resolve.outputs.hf_defaults_path }}" \
+            --artifact-name "dm-model-${{ github.run_number }}" \
+            --model-dir dm_output \
+            --onnx-path "${{ needs.resolve.outputs.onnx_path }}" \
+            --onnx-ref "${{ needs.resolve.outputs.onnx_ref }}" \
+            --model-name "${{ needs.resolve.outputs.model_name }}" \
+            --tinygrad-ref "${{ needs.resolve.outputs.tinygrad_ref }}" \
+            --run-number "${{ github.run_number }}"
+
+  build_dm_model:
+    needs: resolve
+    if: ${{ inputs.target == 'dm' }}
+    runs-on: [self-hosted, tici]
+    env:
+      DM_ONNX: openpilot/selfdrive/modeld/models/dmonitoring_model.onnx
+      DM_PKL: openpilot/selfdrive/modeld/models/dmonitoring_model_tinygrad.pkl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - name: Pull DM ONNX via LFS
+        run: git lfs pull -I "${{ env.DM_ONNX }}"
+
+      - name: Set environment variables
+        run: |
+          source /etc/profile
+          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
+          export UV_PYTHON_PREFERENCE=managed
+          export UV_PYTHON_INSTALL_DIR=${HOME}/uv/python
+          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
+          uv sync --frozen
+          printenv >> $GITHUB_ENV
+
+      - name: Disable powersave
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable
+
+      - name: Compile DM model
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+
+          TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+
+          taskset -c 7 env ${TG_FLAGS} python3 \
+            ${{ github.workspace }}/tinygrad_repo/examples/openpilot/compile3.py \
+            ${{ github.workspace }}/${{ env.DM_ONNX }} \
+            ${{ github.workspace }}/${{ env.DM_PKL }}
+
+      - name: Chunk DM pkl
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          python3 -c "
+          from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+          import os
+          pkl = '${{ github.workspace }}/${{ env.DM_PKL }}'
+          size = os.path.getsize(pkl)
+          targets = get_chunk_targets(pkl, size)
+          chunk_file(pkl, targets)
+          print(f'Chunked {pkl} into {len(targets)} chunks')
+          "
+
+      - name: Compile DM warp
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+
+          TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+          MODEL_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld"
+          DM_SIZE=$(python3 -c "from openpilot.common.transformations.model import DM_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
+
+          for res in $(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')"); do
+            WARP_PKL="${MODEL_DIR}/models/dm_warp_${res}_tinygrad.pkl"
+            taskset -c 7 env ${TG_FLAGS} python3 ${MODEL_DIR}/compile_dm_warp.py \
+              --camera-resolution ${res} \
+              --warp-to ${DM_SIZE} \
+              --output ${WARP_PKL}
+          done
+
+      - name: Prepare DM output
+        run: |
+          mkdir -p dm_output
+          cp ${{ github.workspace }}/${{ env.DM_PKL }}.chunk* dm_output/
+          cp ${{ github.workspace }}/${{ env.DM_PKL }}.chunkmanifest dm_output/
+          cp ${{ github.workspace }}/openpilot/selfdrive/modeld/models/dm_warp_* dm_output/
+
+      - name: Upload DM artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dm-model-${{ github.run_number }}
+          path: dm_output/
+
+      - name: Re-enable powersave
+        if: always()
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
+
@@ -30,7 +30,7 @@ on:
        type: boolean
        default: true
      target_hardware:
-        description: 'Hardware target to compile for (qcom or usbgpu)'
+        description: 'Hardware target to compile for (qcom or chestnut)'
        required: false
        type: string
        default: 'qcom'
@@ -101,7 +101,7 @@ on:
        default: 'qcom'
        options:
          - qcom
-          - usbgpu
+          - chestnut
      hf_repo:
        description: 'Hugging Face dataset repository'
        required: false
@@ -109,7 +109,7 @@ on:
        default: 'sunnypilot/sunnypilot_models_v1'
 env:
  RECOMPILED_DIR: recompiled${{ inputs.recompiled_dir }}
-  JSON_FILE: docs/docs/driving_models_${{ inputs.target_hardware == 'usbgpu' && 'usbgpu_v' || 'v' }}${{ inputs.json_version }}.json
+  JSON_FILE: docs/docs/driving_models_${{ inputs.target_hardware == 'chestnut' && 'chestnut_v' || 'v' }}${{ inputs.json_version }}.json

 jobs:
  build_model:
@@ -146,7 +146,7 @@ jobs:

      - name: Validate hf_repo and JSON version
        env:
-          HF_OIDC_RESOURCE: datasets/${{ inputs.hf_repo }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
        run: |
          if [ ! -f "$JSON_FILE" ]; then
            echo "JSON file $JSON_FILE does not exist!"
@@ -155,13 +155,8 @@ jobs:
          python3 -c "
          import sys
          from huggingface_hub import HfApi
-          try:
-              api = HfApi()
-              api.repo_info(repo_id=sys.argv[1], repo_type='dataset')
-              print(f'Success: Repo {sys.argv[1]} exists.')
-          except Exception as e:
-              print('HF validation failed:', e)
-              sys.exit(1)
+          HfApi().repo_info(repo_id=sys.argv[1], repo_type='dataset')
+          print(f'Success: Repo {sys.argv[1]} exists.')
          " "${{ inputs.hf_repo }}"

      - name: Download artifact name file
@@ -192,7 +187,7 @@ jobs:

      - name: Upload to Hugging Face
        env:
-          HF_OIDC_RESOURCE: datasets/${{ inputs.hf_repo }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
          ARTIFACT_NAME: ${{ steps.read-artifact-name.outputs.artifact_name }}
        run: |
          hf upload ${{ inputs.hf_repo }} \
@@ -0,0 +1,73 @@
+name: Download HF model chunks
+description: Resolve and download model chunks from HuggingFace in parallel
+
+inputs:
+  hf_repo:
+    description: HuggingFace dataset repo
+    required: true
+  models:
+    description: 'JSON array of {hf_path, onnx_hash, canonical} objects'
+    required: true
+  dest_dir:
+    description: Destination directory for downloaded chunks
+    required: true
+
+runs:
+  using: composite
+  steps:
+    - name: Download model chunks
+      shell: bash
+      env:
+        HF_REPO: ${{ inputs.hf_repo }}
+        MODELS_JSON: ${{ inputs.models }}
+        DEST_DIR: ${{ inputs.dest_dir }}
+      run: |
+        set -eo pipefail
+        DOWNLOAD_LIST=$(mktemp)
+
+        resolve_chunks() {
+          local HF_PATH="$1" ONNX_HASH="$2" CANONICAL="$3" DEST_DIR="$4"
+          local JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_PATH}/default_models.json"
+          local DEFAULTS BUNDLE ARTIFACT BASE_URL NUM_CHUNKS
+          DEFAULTS=$(curl -fsSL "$JSON_URL")
+          BUNDLE=$(echo "$DEFAULTS" | jq --arg hash "$ONNX_HASH" '.bundles[] | select(.onnx_sha256 == $hash)')
+          ARTIFACT=$(echo "$BUNDLE" | jq -r '.models[0].artifact')
+          BASE_URL=$(echo "$ARTIFACT" | jq -r '.download_uri.url' | sed 's|/[^/]*$||')
+          NUM_CHUNKS=$(echo "$ARTIFACT" | jq -r '.chunks | length')
+
+          mkdir -p "$DEST_DIR"
+          while IFS= read -r CHUNK_NAME; do
+            CHUNK_IDX=$(echo "$CHUNK_NAME" | grep -oP 'chunk\K[0-9]+of[0-9]+' || true)
+            if [ -z "$CHUNK_IDX" ]; then
+              echo "::error::Failed to parse chunk index from: $CHUNK_NAME"
+              return 1
+            fi
+            ENCODED_URL=$(python3 -c "import urllib.parse; print(urllib.parse.quote('${BASE_URL}/${CHUNK_NAME}', safe=':/'))")
+            printf '%s\t%s\n' "$ENCODED_URL" "${DEST_DIR}/${CANONICAL}.chunk${CHUNK_IDX}" >> "$DOWNLOAD_LIST"
+          done < <(echo "$ARTIFACT" | jq -r '.chunks[].file_name')
+          echo "$NUM_CHUNKS" > "${DEST_DIR}/${CANONICAL}.chunkmanifest"
+
+          if [ "$CANONICAL" = "dmonitoring_model_tinygrad.pkl" ]; then
+            for warp in dm_warp_1928x1208_tinygrad.pkl dm_warp_1344x760_tinygrad.pkl; do
+              ENCODED_URL=$(python3 -c "import urllib.parse; print(urllib.parse.quote('${BASE_URL}/${warp}', safe=':/'))")
+              printf '%s\t%s\n' "$ENCODED_URL" "${DEST_DIR}/${warp}" >> "$DOWNLOAD_LIST"
+            done
+          fi
+        }
+
+        echo "$MODELS_JSON" | jq -c '.[]' | while IFS= read -r model; do
+          HF_PATH=$(echo "$model" | jq -r '.hf_path')
+          ONNX_HASH=$(echo "$model" | jq -r '.onnx_hash')
+          CANONICAL=$(echo "$model" | jq -r '.canonical')
+          resolve_chunks "$HF_PATH" "$ONNX_HASH" "$CANONICAL" "$DEST_DIR"
+        done
+
+        TOTAL=$(wc -l < "$DOWNLOAD_LIST")
+        echo "Downloading $TOTAL chunks with 8 parallel connections..."
+        xargs -P8 -d'\n' -I{} bash -c '
+          URL="${1%%	*}"
+          DEST="${1#*	}"
+          echo "Downloading $(basename "$DEST")"
+          curl -fsSL --retry 3 --retry-delay 5 -o "$DEST" "$URL"
+        ' _ {} < "$DOWNLOAD_LIST"
+        rm -f "$DOWNLOAD_LIST"
@@ -31,7 +31,7 @@ on:
        type: string
        default: ''
      target_hardware:
-        description: 'Hardware target to compile for (qcom or usbgpu)'
+        description: 'Hardware target to compile for (qcom or chestnut)'
        required: false
        type: string
        default: 'qcom'
@@ -57,7 +57,7 @@ on:
        type: choice
        options:
          - qcom
-          - usbgpu
+          - chestnut
        default: 'qcom'


@@ -102,7 +102,7 @@ jobs:
          cat $GITHUB_OUTPUT
      - run: |
          cd ${{ github.workspace }}/openpilot/openpilot
-          if [ "${{ inputs.target_hardware }}" != "usbgpu" ]; then
+          if [ "${{ inputs.target_hardware }}" != "chestnut" ]; then
            git lfs pull -X "**/selfdrive/modeld/models/big_*.onnx,**/selfdrive/modeld/models/dmonitoring_*.onnx"
            rm -f selfdrive/modeld/models/big_*.onnx selfdrive/modeld/models/dmonitoring_*.onnx
          else
@@ -121,7 +121,7 @@ jobs:
          if-no-files-found: error

  build_model:
-    runs-on: [self-hosted, usbgpu]
+    runs-on: [self-hosted, "${{ inputs.target_hardware == 'chestnut' && 'chestnut' || 'tici' }}"]
    needs: get_model
    env:
      MODEL_NAME: ${{ inputs.custom_name || inputs.upstream_branch }} (${{ needs.get_model.outputs.model_date }})
@@ -185,10 +185,10 @@ jobs:
          CAMERA_RES=$(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')")

          TG_FLAGS_QCOM="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
-          if [ "${{ inputs.target_hardware }}" == "usbgpu" ]; then
-            echo "USBGPU build"
-            export USBGPU=1
-            TG_FLAGS="DEBUG=2 DEV=USB+AMD:LLVM WARP_DEV=QCOM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2"
+          if [ "${{ inputs.target_hardware }}" == "chestnut" ]; then
+            echo "CHESTNUT build"
+            export CHESTNUT=1
+            TG_FLAGS="DEBUG=1 DEV=USB+AMD:LLVM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_OCCUPANCY_OPT=1"
            OUTPUT_PKL="${{ env.MODELS_DIR }}/big_driving_tinygrad.pkl"
          else
            echo "QCOM build"
@@ -39,6 +39,8 @@ jobs:
      include_big_model: ${{ steps.strategy.outputs.include_big_model }}
    steps:
      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
      - name: Extract deploy strategy
        id: strategy
        run: |
@@ -96,6 +98,8 @@ jobs:
      }}
    steps:
      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
      - name: Wait for Tests
        uses: ./.github/workflows/wait-for-action # Path to where you place the action
        with:
@@ -119,6 +123,7 @@ jobs:
    steps:
      - uses: actions/checkout@v4
        with:
+          fetch-depth: 1
          submodules: recursive
          ref: ${{ env.SOURCE_BRANCH }}
          repository: ${{ github.event.pull_request.head.repo.fork && github.event.pull_request.head.repo.full_name || github.repository }}
@@ -165,7 +170,7 @@ jobs:
          scons -j1 cache_dir="$SCONS_CACHE" --minimal \
            openpilot/selfdrive/locationd openpilot/sunnypilot/selfdrive/locationd
          echo "Building rest of sunnypilot"
-          /usr/bin/time -v scons -j$(nproc) cache_dir="$SCONS_CACHE" --minimal
+          SKIP_TINYGRAD_COMPILE=1 /usr/bin/time -v scons -j$(nproc) cache_dir="$SCONS_CACHE" --minimal
          touch ${BUILD_DIR}/prebuilt
          if [[ "${{ runner.debug }}" == "1" ]]; then
            ls -la ${BUILD_DIR}
@@ -211,91 +216,245 @@ jobs:
    needs: [ prepare_strategy ]
    runs-on: ubuntu-24.04
    if: ${{ needs.prepare_strategy.outputs.include_big_model == 'true' }}
+    concurrency:
+      group: prepare-chestnut
+      cancel-in-progress: false
+    outputs:
+      onnx_sha256: ${{ steps.resolve.outputs.onnx_sha256 }}
    env:
+      GH_REPO: ${{ github.repository }}
      HF_REPO: sunnypilot/sunnypilot_models_v1
      HF_DEFAULTS_PATH: models/defaults/big
    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref || github.ref_name }}
-
-      - run: git lfs pull -I "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
-
-      - name: Check HF defaults and build if needed
+      - name: Resolve ONNX hash and tinygrad ref via API
        id: resolve
        run: |
-          ACTUAL_ONNX_HASH=$(sha256sum "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx" | cut -d' ' -f1)
-          echo "Repo ONNX hash: $ACTUAL_ONNX_HASH"
+          REF="${{ github.head_ref || github.ref_name }}"
+
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx?ref=${REF}" --jq '.sha')
+          ONNX_HASH=$(gh api "repos/${GH_REPO}/git/blobs/${BLOB_SHA}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
+          echo "ONNX hash: $ONNX_HASH"
+          [ -n "$ONNX_HASH" ] || { echo "::error::Failed to extract ONNX hash"; exit 1; }
+          echo "onnx_sha256=$ONNX_HASH" >> $GITHUB_OUTPUT
+
+          TINYGRAD_REF=$(gh api "repos/${GH_REPO}/contents/tinygrad_repo?ref=${REF}" --jq '.sha')
+          echo "tinygrad ref: $TINYGRAD_REF"

          JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"

-          check_hash() {
-            DEFAULTS=$(curl -fsSL "$JSON_URL" 2>/dev/null) || return 1
-            BUNDLE=$(echo "$DEFAULTS" | jq --arg hash "$ACTUAL_ONNX_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
+          check_defaults() {
+            DEFAULTS=$(curl -fsSL "${JSON_URL}?t=$(date +%s)" 2>/dev/null) || return 1
+            TINYGRAD_MATCH=$(echo "$DEFAULTS" | jq -r --arg ref "$TINYGRAD_REF" '.tinygrad_ref == $ref' 2>/dev/null)
+            [ "$TINYGRAD_MATCH" = "true" ] || return 1
+            BUNDLE=$(echo "$DEFAULTS" | jq --arg hash "$ONNX_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
            [ -n "$BUNDLE" ] && [ "$BUNDLE" != "null" ]
          }

-          if check_hash; then
-            echo "HF defaults match repo ONNX"
-          else
-            echo "No matching model on HF — triggering build"
-            gh workflow run build-default-big-model.yaml --ref "${{ github.head_ref || github.ref_name }}"
-
-            echo "Waiting for build to start..."
-            sleep 120
-
-            RUN_ID=$(gh run list --workflow=build-default-big-model.yaml --branch="${{ github.head_ref || github.ref_name }}" --limit=1 --json databaseId --jq '.[0].databaseId')
-            if [ -z "$RUN_ID" ] || [ "$RUN_ID" = "null" ]; then
-              echo "::error::Failed to find build-default-big-model run"
-              exit 1
-            fi
-
-            echo "Waiting for run $RUN_ID..."
-            gh run watch "$RUN_ID"
-
-            CONCLUSION=$(gh run view "$RUN_ID" --json conclusion --jq '.conclusion')
-            if [ "$CONCLUSION" != "success" ]; then
-              echo "::error::build-default-big-model failed: $CONCLUSION"
-              exit 1
-            fi
-
-            if ! check_hash; then
-              echo "::error::HF defaults still don't match after build"
-              exit 1
-            fi
+          if check_defaults; then
+            echo "HF defaults match repo ONNX hash and tinygrad ref"
+            exit 0
          fi
+
+          echo "No matching model on HF — dispatching build"
+          gh workflow run build-default-models.yaml --ref "$REF" -f target=big
+          sleep 10
+
+          BUILD_RUN_ID=$(gh run list --workflow build-default-models.yaml --branch "$REF" --limit 1 --json databaseId --jq '.[0].databaseId')
+          echo "Dispatched build run: $BUILD_RUN_ID"
+
+          echo "Waiting for build run to complete..."
+          for i in $(seq 1 90); do
+            sleep 30
+            STATUS=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.status')
+            CONCLUSION=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.conclusion')
+            echo "Poll $i/90: status=$STATUS conclusion=$CONCLUSION"
+            if [ "$STATUS" = "completed" ]; then
+              if [ "$CONCLUSION" = "success" ]; then
+                echo "Build run succeeded, verifying HF..."
+                sleep 10
+                if check_defaults; then
+                  echo "Big model verified on HF"
+                  exit 0
+                fi
+                echo "::error::Build succeeded but model not found on HF"
+                exit 1
+              else
+                echo "::error::Build run failed with conclusion=$CONCLUSION"
+                exit 1
+              fi
+            fi
+          done
+
+          echo "::error::Build run did not complete within 45 minutes"
+          exit 1
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

-      - name: Download big model chunks
+      - name: Cancel run on failure
+        if: failure()
+        run: gh run cancel ${{ github.run_id }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  prepare_small_model:
+    needs: [ prepare_strategy ]
+    runs-on: ubuntu-24.04
+    concurrency:
+      group: prepare-small-model
+      cancel-in-progress: false
+    outputs:
+      driving_onnx_sha256: ${{ steps.resolve.outputs.driving_onnx_sha256 }}
+    env:
+      GH_REPO: ${{ github.repository }}
+      HF_REPO: sunnypilot/sunnypilot_models_v1
+      HF_DEFAULTS_PATH: models/defaults/small
+    steps:
+      - name: Resolve ONNX hash and tinygrad ref via API
+        id: resolve
        run: |
-          ACTUAL_ONNX_HASH=$(sha256sum "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx" | cut -d' ' -f1)
+          REF="${{ github.head_ref || github.ref_name }}"
+
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/driving_supercombo.onnx?ref=${REF}" --jq '.sha')
+          DRIVING_HASH=$(gh api "repos/${GH_REPO}/git/blobs/${BLOB_SHA}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
+          echo "Driving ONNX hash: $DRIVING_HASH"
+          [ -n "$DRIVING_HASH" ] || { echo "::error::Failed to extract driving ONNX hash"; exit 1; }
+          echo "driving_onnx_sha256=$DRIVING_HASH" >> $GITHUB_OUTPUT
+
+          TINYGRAD_REF=$(gh api "repos/${GH_REPO}/contents/tinygrad_repo?ref=${REF}" --jq '.sha')
+          echo "tinygrad ref: $TINYGRAD_REF"
+
          JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
-          DEFAULTS=$(curl -fsSL "$JSON_URL")
-          BUNDLE=$(echo "$DEFAULTS" | jq --arg hash "$ACTUAL_ONNX_HASH" '.bundles[] | select(.onnx_sha256 == $hash)')

-          mkdir -p big_model_chunks
-          ARTIFACT=$(echo "$BUNDLE" | jq -r '.models[0].artifact')
-          BASE_URL=$(echo "$ARTIFACT" | jq -r '.download_uri.url' | sed 's|/[^/]*$||')
-          NUM_CHUNKS=$(echo "$ARTIFACT" | jq -r '.chunks | length')
+          check_defaults() {
+            DEFAULTS=$(curl -fsSL "${JSON_URL}?t=$(date +%s)" 2>/dev/null) || return 1
+            TINYGRAD_MATCH=$(echo "$DEFAULTS" | jq -r --arg ref "$TINYGRAD_REF" '.tinygrad_ref == $ref' 2>/dev/null)
+            [ "$TINYGRAD_MATCH" = "true" ] || return 1
+            DRIVING=$(echo "$DEFAULTS" | jq --arg hash "$DRIVING_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
+            [ -n "$DRIVING" ] && [ "$DRIVING" != "null" ] || return 1
+          }

-          CANONICAL="big_driving_tinygrad.pkl"
-          echo "$ARTIFACT" | jq -r '.chunks[].file_name' | while read CHUNK_NAME; do
-            CHUNK_IDX=$(echo "$CHUNK_NAME" | grep -oP 'chunk\K[0-9]+of[0-9]+')
-            CANONICAL_CHUNK="${CANONICAL}.chunk${CHUNK_IDX}"
-            ENCODED_URL=$(python3 -c "import urllib.parse; print(urllib.parse.quote('${BASE_URL}/${CHUNK_NAME}', safe=':/'))")
-            echo "Downloading $CHUNK_NAME -> $CANONICAL_CHUNK"
-            curl -fsSL -o "big_model_chunks/${CANONICAL_CHUNK}" "$ENCODED_URL"
+          if check_defaults; then
+            echo "HF defaults match repo ONNX hash and tinygrad ref"
+            exit 0
+          fi
+
+          echo "No matching model on HF — dispatching build"
+          gh workflow run build-default-models.yaml --ref "$REF" -f target=small
+          sleep 10
+
+          BUILD_RUN_ID=$(gh run list --workflow build-default-models.yaml --branch "$REF" --limit 1 --json databaseId --jq '.[0].databaseId')
+          echo "Dispatched build run: $BUILD_RUN_ID"
+
+          echo "Waiting for build run to complete..."
+          for i in $(seq 1 60); do
+            sleep 30
+            STATUS=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.status')
+            CONCLUSION=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.conclusion')
+            echo "Poll $i/60: status=$STATUS conclusion=$CONCLUSION"
+            if [ "$STATUS" = "completed" ]; then
+              if [ "$CONCLUSION" = "success" ]; then
+                echo "Build run succeeded, verifying HF..."
+                sleep 10
+                if check_defaults; then
+                  echo "Small model verified on HF"
+                  exit 0
+                fi
+                echo "::error::Build succeeded but model not found on HF"
+                exit 1
+              else
+                echo "::error::Build run failed with conclusion=$CONCLUSION"
+                exit 1
+              fi
+            fi
          done

-          echo "$NUM_CHUNKS" > "big_model_chunks/${CANONICAL}.chunkmanifest"
+          echo "::error::Small model build did not complete within 30 minutes"
+          exit 1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

-      - name: Upload big model chunks
-        uses: actions/upload-artifact@v4
-        with:
-          name: big-model-chunks
-          path: big_model_chunks/
-          compression-level: 0
+      - name: Cancel run on failure
+        if: failure()
+        run: gh run cancel ${{ github.run_id }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  prepare_dm_model:
+    needs: [ prepare_strategy ]
+    runs-on: ubuntu-24.04
+    concurrency:
+      group: prepare-dm-model
+      cancel-in-progress: false
+    outputs:
+      dm_onnx_sha256: ${{ steps.resolve.outputs.dm_onnx_sha256 }}
+    env:
+      GH_REPO: ${{ github.repository }}
+      HF_REPO: sunnypilot/sunnypilot_models_v1
+      HF_DEFAULTS_PATH: models/defaults/dm
+    steps:
+      - name: Resolve ONNX hash and tinygrad ref via API
+        id: resolve
+        run: |
+          REF="${{ github.head_ref || github.ref_name }}"
+
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/dmonitoring_model.onnx?ref=${REF}" --jq '.sha')
+          DM_HASH=$(gh api "repos/${GH_REPO}/git/blobs/${BLOB_SHA}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
+          echo "DM ONNX hash: $DM_HASH"
+          [ -n "$DM_HASH" ] || { echo "::error::Failed to extract DM ONNX hash"; exit 1; }
+          echo "dm_onnx_sha256=$DM_HASH" >> $GITHUB_OUTPUT
+
+          TINYGRAD_REF=$(gh api "repos/${GH_REPO}/contents/tinygrad_repo?ref=${REF}" --jq '.sha')
+          echo "tinygrad ref: $TINYGRAD_REF"
+
+          JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
+
+          check_defaults() {
+            DEFAULTS=$(curl -fsSL "${JSON_URL}?t=$(date +%s)" 2>/dev/null) || return 1
+            TINYGRAD_MATCH=$(echo "$DEFAULTS" | jq -r --arg ref "$TINYGRAD_REF" '.tinygrad_ref == $ref' 2>/dev/null)
+            [ "$TINYGRAD_MATCH" = "true" ] || return 1
+            DM=$(echo "$DEFAULTS" | jq --arg hash "$DM_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
+            [ -n "$DM" ] && [ "$DM" != "null" ] || return 1
+          }
+
+          if check_defaults; then
+            echo "HF defaults match DM ONNX hash and tinygrad ref"
+            exit 0
+          fi
+
+          echo "No matching DM model on HF — dispatching build"
+          gh workflow run build-default-models.yaml --ref "$REF" -f target=dm
+          sleep 10
+
+          BUILD_RUN_ID=$(gh run list --workflow build-default-models.yaml --branch "$REF" --limit 1 --json databaseId --jq '.[0].databaseId')
+          echo "Dispatched build run: $BUILD_RUN_ID"
+
+          echo "Waiting for build run to complete..."
+          for i in $(seq 1 60); do
+            sleep 30
+            STATUS=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.status')
+            CONCLUSION=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.conclusion')
+            echo "Poll $i/60: status=$STATUS conclusion=$CONCLUSION"
+            if [ "$STATUS" = "completed" ]; then
+              if [ "$CONCLUSION" = "success" ]; then
+                echo "Build run succeeded, verifying HF..."
+                sleep 10
+                if check_defaults; then
+                  echo "DM model verified on HF"
+                  exit 0
+                fi
+                echo "::error::Build succeeded but DM model not found on HF"
+                exit 1
+              else
+                echo "::error::Build run failed with conclusion=$CONCLUSION"
+                exit 1
+              fi
+            fi
+          done
+
+          echo "::error::DM model build did not complete within 30 minutes"
+          exit 1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

      - name: Cancel run on failure
        if: failure()
@@ -305,23 +464,24 @@ jobs:

  publish:
    concurrency:
-      # We do a bit of a hack here to avoid canceling the publishing job if a new commit comes in while we're publishing by adding the sha to the group name.
-      # This means that if multiple commits come in while we're publishing, they will be queued up and publish one after the other.
-      # Otherwise, if a job is waiting to be published due to environment wait time, it would be canceled by a new commit and restart the wait time.
      group: ${{ needs.prepare_strategy.outputs.publish_concurrency_group }}
      cancel-in-progress: ${{ needs.prepare_strategy.outputs.cancel_publish_in_progress == 'true' }}
    if: ${{
      always() && !cancelled() &&
      needs.build.result == 'success' &&
      needs.prepare_strategy.result == 'success' &&
+      needs.prepare_small_model.result == 'success' &&
+      needs.prepare_dm_model.result == 'success' &&
      (!contains(github.event_name, 'pull_request') || (github.event.action == 'labeled' && github.event.label.name == 'prebuilt')) &&
      (needs.prepare_strategy.outputs.include_big_model != 'true' || needs.prepare_chestnut.result == 'success')
      }}
-    needs: [ build, prepare_strategy, prepare_chestnut ]
+    needs: [ build, prepare_strategy, prepare_chestnut, prepare_small_model, prepare_dm_model ]
    runs-on: ubuntu-24.04
    environment: ${{ needs.prepare_strategy.outputs.environment }}
    steps:
      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1

      - name: Download prebuilt artifact
        uses: actions/download-artifact@v4
@@ -333,23 +493,16 @@ jobs:
          mkdir -p ${{ env.OUTPUT_DIR }}
          tar xzf prebuilt.tar.gz -C ${{ env.OUTPUT_DIR }}

-      - name: Prepare chestnut output
-        if: ${{ needs.prepare_chestnut.result == 'success' }}
-        run: |
-          mkdir -p "${{ github.workspace }}/chestnut_output"
-          tar xzf prebuilt.tar.gz -C "${{ github.workspace }}/chestnut_output"
-
-      - name: Download big model chunks
-        if: ${{ needs.prepare_chestnut.result == 'success' }}
-        uses: actions/download-artifact@v4
+      - name: Download model chunks from HF
+        uses: ./.github/workflows/download-hf-model-chunks
        with:
-          name: big-model-chunks
-          path: big_model_chunks
-
-      - name: Inject big model into chestnut
-        if: ${{ needs.prepare_chestnut.result == 'success' }}
-        run: |
-          cp big_model_chunks/* "${{ github.workspace }}/chestnut_output/openpilot/selfdrive/modeld/models/"
+          hf_repo: sunnypilot/sunnypilot_models_v1
+          dest_dir: ${{ env.OUTPUT_DIR }}/openpilot/selfdrive/modeld/models
+          models: |
+            [
+              {"hf_path": "models/defaults/small", "onnx_hash": "${{ needs.prepare_small_model.outputs.driving_onnx_sha256 }}", "canonical": "driving_tinygrad.pkl"},
+              {"hf_path": "models/defaults/dm", "onnx_hash": "${{ needs.prepare_dm_model.outputs.dm_onnx_sha256 }}", "canonical": "dmonitoring_model_tinygrad.pkl"}
+            ]

      - name: Configure Git
        run: |
@@ -371,22 +524,6 @@ jobs:
            "https://x-access-token:${{github.token}}@github.com/sunnypilot/sunnypilot.git" \
            "${{ needs.prepare_strategy.outputs.extra_version_identifier }}"

-      - name: Publish chestnut branch
-        if: ${{ needs.prepare_chestnut.result == 'success' }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          CHESTNUT_BRANCH="${{ needs.prepare_strategy.outputs.new_branch }}-chestnut"
-          CHESTNUT_DIR="${{ github.workspace }}/chestnut_output"
-
-          ${{ env.CI_DIR }}/publish.sh \
-            "${{ github.workspace }}" \
-            "$CHESTNUT_DIR" \
-            "$CHESTNUT_BRANCH" \
-            "${{ needs.prepare_strategy.outputs.version }}" \
-            "https://x-access-token:${{github.token}}@github.com/sunnypilot/sunnypilot.git" \
-            "${{ needs.prepare_strategy.outputs.extra_version_identifier }}"
-
      - name: Tag ${{ needs.prepare_strategy.outputs.environment }}
        if: ${{ needs.prepare_strategy.outputs.is_stable_branch == 'true' && (github.event_name != 'push' || !startsWith(github.ref, 'refs/tags/')) }}
        run: |
@@ -394,12 +531,77 @@ jobs:
            git tag -f -a ${TAG} -m "${{ needs.prepare_strategy.outputs.environment }} @ ${{ needs.prepare_strategy.outputs.version }} of build ${{ needs.prepare_strategy.outputs.build }}."
            git push -f origin ${TAG}

+  publish_chestnut:
+    concurrency:
+      group: ${{ needs.prepare_strategy.outputs.publish_concurrency_group }}-chestnut
+      cancel-in-progress: ${{ needs.prepare_strategy.outputs.cancel_publish_in_progress == 'true' }}
+    if: ${{
+      always() && !cancelled() &&
+      needs.build.result == 'success' &&
+      needs.prepare_strategy.result == 'success' &&
+      needs.prepare_small_model.result == 'success' &&
+      needs.prepare_dm_model.result == 'success' &&
+      needs.prepare_chestnut.result == 'success' &&
+      (!contains(github.event_name, 'pull_request') || (github.event.action == 'labeled' && github.event.label.name == 'prebuilt'))
+      }}
+    needs: [ build, prepare_strategy, prepare_chestnut, prepare_small_model, prepare_dm_model ]
+    runs-on: ubuntu-24.04
+    environment: ${{ needs.prepare_strategy.outputs.environment }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Download prebuilt artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: prebuilt
+
+      - name: Untar prebuilt
+        run: |
+          mkdir -p ${{ env.OUTPUT_DIR }}
+          tar xzf prebuilt.tar.gz -C ${{ env.OUTPUT_DIR }}
+
+      - name: Download model chunks from HF
+        uses: ./.github/workflows/download-hf-model-chunks
+        with:
+          hf_repo: sunnypilot/sunnypilot_models_v1
+          dest_dir: ${{ env.OUTPUT_DIR }}/openpilot/selfdrive/modeld/models
+          models: |
+            [
+              {"hf_path": "models/defaults/small", "onnx_hash": "${{ needs.prepare_small_model.outputs.driving_onnx_sha256 }}", "canonical": "driving_tinygrad.pkl"},
+              {"hf_path": "models/defaults/dm", "onnx_hash": "${{ needs.prepare_dm_model.outputs.dm_onnx_sha256 }}", "canonical": "dmonitoring_model_tinygrad.pkl"},
+              {"hf_path": "models/defaults/big", "onnx_hash": "${{ needs.prepare_chestnut.outputs.onnx_sha256 }}", "canonical": "big_driving_tinygrad.pkl"}
+            ]
+
+      - name: Configure Git
+        run: |
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          git config --global user.name "github-actions[bot]"
+
+      - name: Publish chestnut branch
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          CHESTNUT_BRANCH="${{ needs.prepare_strategy.outputs.new_branch }}-chestnut"
+
+          ${{ env.CI_DIR }}/publish.sh \
+            "${{ github.workspace }}" \
+            "${{ env.OUTPUT_DIR }}" \
+            "$CHESTNUT_BRANCH" \
+            "${{ needs.prepare_strategy.outputs.version }}" \
+            "https://x-access-token:${{github.token}}@github.com/sunnypilot/sunnypilot.git" \
+            "${{ needs.prepare_strategy.outputs.extra_version_identifier }}"
+
  notify:
    needs:
    - prepare_strategy
    - build
    - publish
+    - publish_chestnut
    - prepare_chestnut
+    - prepare_small_model
+    - prepare_dm_model
    runs-on: ubuntu-24.04
    if: ${{ (always() && !cancelled() && !failure())
      && needs.publish.result == 'success'
@@ -407,6 +609,8 @@ jobs:
      && (fromJSON(vars.DEV_FEEDBACK_NOTIFICATION_BRANCHES_V2)[github.head_ref || github.ref_name] != null) }}
    steps:
      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1

      - name: Prepare notification message
        id: message
@@ -0,0 +1,124 @@
+# Ford C1 feedback experiment
+
+The restored original v1 can leave a steering error while C0 and C1 still have
+room. Its command law does not directly correct measured steering error. This
+experiment keeps that mapping and adds one accumulated C1 correction:
+
+```text
+error = selected_limited_desired_curvature - measured_curvature
+correction += error * speed * elapsed_measurement_time
+C1_target = original_model_C1 + correction
+```
+
+Curvature (1/m) multiplied by traveled distance (m) gives heading mismatch in
+radians. Applying that mismatch to C1 at **1:1 is an explicit feedback-strength
+choice**. Dimensional consistency does not prove that every PSCM responds
+correctly to that strength. There is no fitted PSCM response model or new
+tunable multiplier.
+
+For example, at 20 m/s, a constant curvature shortfall of 0.001/m adds 0.02 rad
+to C1 over one second when the output can accept it. When measured curvature
+matches the request, the correction holds. If the vehicle turns more than
+requested, the correction moves in the unwind direction. Changing the model
+request still changes the base immediately, subject to the existing slew.
+
+## Preserved mapping and limits
+
+- C0 is the current model path's lateral offset at 7 m of arc distance, holding
+  the available endpoint for shorter paths; its limits remain ±5.11 m and 4 m/s.
+- Base C1 is `max(7 m, speed × 1 s) × selected_limited_desired_curvature`, clipped
+  to ±0.5 rad. Final C1 uses the same ±0.5 rad and 0.5 rad/s limits as v1.
+- C2 and C3 are zero. Sign conversion, Float32/CAN rounding, upstream curvature
+  limiting and the 100 Hz sender retain their existing behavior.
+
+The core holds three values: unquantized C0, unquantized C1 and the correction.
+Zero error from a reset leaves the correction at zero and preserves the old
+command arithmetic exactly. There is no separate percentage or distance cap
+on the correction.
+
+## Feedback measurement, timing and limits
+
+The measurement is `controlsd.curvature`, computed from measured steering
+angle with the existing live vehicle parameters. It matches the curvature
+used for the desired-versus-actual steering comparison. It is not an independent
+measurement of tire slip or the vehicle's actual ground path. CAN yaw remains
+an input-health gate and does not drive this feedback.
+
+The adapter integrates only elapsed time between fresh `carState` publications.
+The first publication after reset integrates zero time. Duplicate timestamps
+integrate zero; a fresh timestamp accounts for the elapsed measurement interval.
+Output slew continues on valid control cycles. Existing service-age, speed,
+model-geometry and clock-order gates remain, with the same finite/range check
+also applied to measured curvature. Disengagement or invalid input clears all
+three core states.
+
+The correction cannot accumulate farther into an unavailable C1 amplitude or
+slew request. Increments that move back toward the available output remain
+allowed. Moving the base request does not itself rewrite the correction.
+
+Fresh PSCM status means a valid message whose original CAN receipt timestamp
+is within the existing −5 to +150 ms age allowance. Reached-limit status (2)
+prevents extra accumulation in the measured turn direction. An old correction
+opposing that direction can return to zero; it cannot be trapped below the
+base request by the limit flag. Unwind and base model changes remain available.
+Close-to-limit status (1) does not block feedback. Missing or stale status
+does not gate it; local amplitude and slew anti-windup still apply.
+
+Driver steering-pressed, torque above the existing 1 Nm allowance, nonfinite
+torque, or fresh driver-limit status (3) clears the correction. Fresh denied
+or inactive PSCM status also clears it. The base model request continues
+through existing engagement and driver arbitration; clearing the correction
+does not bypass the final output slew.
+
+## Offline evidence and reproduction
+
+`ford_c1_feedback_validation.json` records the source hashes and completed
+checks. Tests exercise build, hold, unwind, saturation, limit flags, immediate
+driver input, stale and repeated measurements, invalid inputs and both signs.
+Integration tests execute actual controlsd selection and limiting, Float32
+publication, CarControlSP conversion and the Ford CarController CAN builder.
+Randomized runs check feedback invariants separately from zero-error
+compatibility with the original independent scalar oracle.
+
+The combined suite passes **511 tests and 9,146 subtests**. Its 178 skips are
+in inherited safety base classes or unsupported safety-test variants. Ruff,
+the controller's Ty check and settings compilation pass. Feedback stress,
+zero-error stress and the b8 replay total **578,569 Float32/CAN round trips**;
+the integration test separately verifies 1,010 transmitted packet constructions,
+including every counter and checksum. No packets are sent to hardware.
+
+The b8 replay retains recorded desired/measured curvature, model publications,
+driver input and PSCM flags. It compares candidate commands with the restored
+v1 at `a7d70e2b0890184636827351e4789d866f2a7c97`. All 160,431 reconstructed
+activation decisions and C0 commands match. C1 changes on 58,106 cycles.
+At 4:12.493, for example, reconstructed host C1 changes from −0.1625 to
+−0.2035 rad; at 3:56.250 it changes from −0.1280 to −0.1080 rad. These are
+changes to commands on frozen measurements, not predicted wheel angles.
+
+Controls publication time proxies the unlogged computation clock, and the
+full SubMaster health state cannot be reconstructed. This route uses the
+consumed model publication as its reference and has no maneuver-plan messages.
+Replay cannot show whether this feedback fixes weak turns, hanging turns or
+oscillation. A new drive is needed to measure those outcomes.
+
+Use the branch's native dependencies and pinned opendbc revision
+`c21a9013700734dd20b09e05aa68329ad8cc20f9`:
+
+```sh
+export PYTHONDONTWRITEBYTECODE=1
+export PYTHONPATH=.:opendbc_repo
+python -m pytest -q -p no:cacheprovider openpilot/selfdrive/controls/tests/test_ford_*.py tools/ford_pscm_lab openpilot/selfdrive/car/tests/test_ford_pscm_status.py openpilot/sunnypilot/sunnylink/tests openpilot/common/tests/test_params.py opendbc_repo/opendbc/car/ford/tests/test_ford.py opendbc_repo/opendbc/safety/tests/test_ford.py
+python openpilot/sunnypilot/sunnylink/tools/compile_settings_ui.py --check
+python -m tools.ford_pscm_lab.feedback_replay stress --cycles 200000 --output .cache/ford_c1_feedback/feedback_stress.json
+python -m tools.ford_pscm_lab.stress_model_action --cycles 200000 --seed 20260907 --opendbc-revision c21a9013700734dd20b09e05aa68329ad8cc20f9 --output .cache/ford_c1_feedback/zero_error_stress.json
+python -m tools.ford_pscm_lab.feedback_replay route .cache/ford_routeb8 --output .cache/ford_c1_feedback/routeb8
+```
+
+The last command requires the existing full-rlog b8 extract (`route.npz`,
+`model_paths.npz`, `metadata.json`), identified by hashes in the validation
+record. The historical route90/95 replay deliberately sets measured curvature
+equal to requested curvature to check zero-error compatibility; it does not
+exercise recorded steering feedback.
+
+Enable using the [existing Sunnylink toggle](ford_model_action_drive_test.md).
+The diagnostic identity is `model-action-c1-feedback-v1`.
@@ -0,0 +1,258 @@
+{
+  "created_at_utc": "2026-09-09T14:45:33.853345+00:00",
+  "baseline_commit": "a7d70e2b0890184636827351e4789d866f2a7c97",
+  "deployment_target": {
+    "repository": "sunnypilot/sunnypilot",
+    "branch": "hiimisaac-dev"
+  },
+  "scope": "C1 measured-curvature feedback on restored original v1. Offline software validation only; no predicted or measured physical improvement.",
+  "calibration_approved": false,
+  "toggle": {
+    "key": "FordModelActionController",
+    "default_enabled": false,
+    "activation": "Existing startup selection after offroad-to-onroad cycle"
+  },
+  "feedback_law": "correction += (desired_curvature - measured_curvature) * speed * elapsed_measurement_time, subject to output and PSCM anti-windup",
+  "feedback_strength": "Explicit 1:1 heading-error-to-C1 choice; no fitted PSCM plant or new tunable multiplier",
+  "preserved": [
+    "C0 mapping and limits",
+    "C2=C3=0",
+    "C1 final amplitude and slew limits",
+    "100 Hz sender",
+    "upstream selection and limiting"
+  ],
+  "panda_safety_changed": false,
+  "opendbc_submodule_changed": false,
+  "opendbc_head": "c21a9013700734dd20b09e05aa68329ad8cc20f9",
+  "controller_size": {
+    "total_lines": 181,
+    "code_lines_excluding_blanks_comments_docstrings": 123,
+    "core_persistent_values": 3,
+    "adapter_timestamps": 3
+  },
+  "tests": {
+    "combined_suite": "511 passed, 178 skipped, 9146 subtests passed in 5.14s",
+    "suite_log_sha256": "001ef6633b22513317593dd8debc160a0ca8aaf78ea53418c5f7a50c370cc818",
+    "ruff_changed_python": "pass",
+    "ty_controller": "pass",
+    "settings_compiler_check": "pass",
+    "safety_skip_reasons": [
+      "SKIPPED [145] ../../../../dev/sunnypilot/.venv/lib/python3.12/site-packages/_pytest/unittest.py:523: Skipped",
+      "SKIPPED [9] opendbc_repo/opendbc/safety/tests/common.py:64: Safety mode implements no _user_regen_msg",
+      "SKIPPED [3] opendbc_repo/opendbc/safety/tests/mads_common.py:51: Skipping test because MADS button is not supported",
+      "SKIPPED [3] opendbc_repo/opendbc/safety/tests/mads_common.py:254: Skipping test because MADS button is not supported",
+      "SKIPPED [3] opendbc_repo/opendbc/safety/tests/mads_common.py:67: Skipping test because _acc_state_msg is not implemented for this car",
+      "SKIPPED [3] opendbc_repo/opendbc/safety/tests/mads_common.py:165: Skipping test because MADS button is not supported",
+      "SKIPPED [3] opendbc_repo/opendbc/safety/tests/mads_common.py:165: Skipping test because ACC main is not supported",
+      "SKIPPED [3] opendbc_repo/opendbc/safety/tests/mads_common.py:411: MADS button not supported",
+      "SKIPPED [1] opendbc_repo/opendbc/safety/tests/test_ford.py:378: CAN FD only",
+      "SKIPPED [1] opendbc_repo/opendbc/safety/tests/test_ford.py:361: CAN FD only",
+      "SKIPPED [1] opendbc_repo/opendbc/safety/tests/test_ford.py:351: CAN FD only",
+      "SKIPPED [1] opendbc_repo/opendbc/safety/tests/test_ford.py:327: CAN FD only",
+      "SKIPPED [1] opendbc_repo/opendbc/safety/tests/test_ford.py:341: CAN FD only",
+      "SKIPPED [1] opendbc_repo/opendbc/safety/tests/test_ford.py:320: CAN FD only"
+    ],
+    "safety_native_build": "Pinned safety C source is compiled locally by libsafety_py before testing.",
+    "controlsd_to_can_feedback_integration_frames": 1010,
+    "integration_checks": "Both signs: build, hold, unwind, rebuild, immediate driver override; actual 100 Hz sender, counter, checksum, fields and publication. Separate integration tests validate PSCM service forwarding.",
+    "regression_test_evidence": [
+      "Nonzero-error integration failed with zero correction before implementing feedback.",
+      "Both sign tests failed when a reached limit trapped an old opposing correction; they pass after allowing return to zero."
+    ]
+  },
+  "route_b8": {
+    "baseline_revision": "a7d70e2b0890184636827351e4789d866f2a7c97",
+    "baseline_source_sha256": "8f3bc5d68e0051776f614a2ccffae84a88f7898dc95bdc12c23dcfe10dfe676a",
+    "cycles": 160431,
+    "active_cycles": 68217,
+    "validity_and_c0_match_original_v1_exactly": true,
+    "status_counts": {
+      "inactive": 92214,
+      "active": 68217
+    },
+    "feedback_enabled_seconds": 598.256888772994,
+    "pscm_limit_2_seconds": 12.139079590997426,
+    "c1_changed_cycles": 58106,
+    "max_abs_c1_change_rad": 0.29800000000000004,
+    "max_abs_correction_rad": 0.29816844327770786,
+    "can_round_trips": 160431,
+    "timing_limit": "Controls publication time proxies the computation clock; full SubMaster checks are unavailable.",
+    "reference_limit": "Uses exact consumed model publication as reference; the b8 route has no maneuver-plan messages.",
+    "example_points": [
+      {
+        "time_s": 130.9368894940053,
+        "old_c0_c1": [
+          -0.7400000000000002,
+          -0.18700000000000006
+        ],
+        "candidate_c0_c1": [
+          -0.7400000000000002,
+          -0.22899999999999998
+        ],
+        "correction_rad": -0.042171663052515254,
+        "feedback_enabled": true,
+        "pscm_limited": false
+      },
+      {
+        "time_s": 235.3960996990063,
+        "old_c0_c1": [
+          -2.04,
+          -0.40449999999999997
+        ],
+        "candidate_c0_c1": [
+          -2.04,
+          -0.4145
+        ],
+        "correction_rad": -0.00989648519895422,
+        "feedback_enabled": true,
+        "pscm_limited": true
+      },
+      {
+        "time_s": 236.25034470800165,
+        "old_c0_c1": [
+          -1.46,
+          -0.128
+        ],
+        "candidate_c0_c1": [
+          -1.46,
+          -0.10799999999999998
+        ],
+        "correction_rad": 0.01990758350705991,
+        "feedback_enabled": true,
+        "pscm_limited": false
+      },
+      {
+        "time_s": 252.49320156300382,
+        "old_c0_c1": [
+          -0.6699999999999999,
+          -0.16249999999999998
+        ],
+        "candidate_c0_c1": [
+          -0.6699999999999999,
+          -0.20350000000000001
+        ],
+        "correction_rad": -0.040907632902654506,
+        "feedback_enabled": true,
+        "pscm_limited": false
+      },
+      {
+        "time_s": 674.430371745002,
+        "old_c0_c1": [
+          0.4299999999999997,
+          0.128
+        ],
+        "candidate_c0_c1": [
+          0.4299999999999997,
+          0.1345
+        ],
+        "correction_rad": 0.00639271291315417,
+        "feedback_enabled": true,
+        "pscm_limited": false
+      },
+      {
+        "time_s": 1534.5190040400048,
+        "old_c0_c1": [
+          2.62,
+          0.5
+        ],
+        "candidate_c0_c1": [
+          2.62,
+          0.5
+        ],
+        "correction_rad": 0.0,
+        "feedback_enabled": true,
+        "pscm_limited": true
+      },
+      {
+        "time_s": 1562.5074677500015,
+        "old_c0_c1": [
+          -0.1200000000000001,
+          -0.051000000000000045
+        ],
+        "candidate_c0_c1": [
+          -0.1200000000000001,
+          -0.046499999999999986
+        ],
+        "correction_rad": 0.004453988923883501,
+        "feedback_enabled": true,
+        "pscm_limited": false
+      }
+    ]
+  },
+  "route_input_sha256": {
+    "route.npz": "6f5dd369b70eaed4b95b28c8b25c9f2e9b830fa07a334881a185505481667c8b",
+    "model_paths.npz": "939af6cf7e74251d8842581cc078d26d9fbfd22a0d7817cb0e368697d419b615",
+    "metadata.json": "73b439132d1de37ec187b544c04d2b05c80965065515a4b7dec29ba57ae37e7c"
+  },
+  "feedback_stress": {
+    "cycles": 200000,
+    "mirrored_updates": 200000,
+    "can_round_trips": 200000,
+    "checks": "Mirror symmetry, reset/override, amplitude, slew, correction bounds, integration direction/size, PSCM anti-windup, CAN fields.",
+    "scope": "Numerical software invariants only; no model of vehicle motion.",
+    "calibration_approved": false,
+    "controller_sha256": "4499defbb7fc5ddf5029ca42c549f0935b0758b08818c5bf0490fb52221f9a34"
+  },
+  "zero_error_stress": {
+    "seed": 20260907,
+    "random_cycles": 200000,
+    "mirrored_core_updates": 200000,
+    "invalid_or_inactive_resets": 3537,
+    "field_boundary_cases": 18138,
+    "float32_can_round_trips": 218138,
+    "analytic_targets_scalar_slew_and_mirror_checks_pass": true,
+    "direct_raw_float32_packing_matches_host_output": true,
+    "max_continuous_step_c0_c1": [
+      0.40000000000000147,
+      0.05000000000000002
+    ],
+    "calibration_approved": false,
+    "scope": "Zero-error numerical construction: measured equals requested curvature. No PSCM response claims.",
+    "opendbc_import_head": "c21a9013700734dd20b09e05aa68329ad8cc20f9"
+  },
+  "total_lab_float32_can_round_trips": 578569,
+  "artifact_sha256": {
+    ".cache/ford_c1_feedback/routeb8/report.json": "648887eebb76260a60f7c0f0d9aaac83d0f1c06b443e28bc6fdf296bad4526c0",
+    ".cache/ford_c1_feedback/routeb8/commands.npz": "1aef98365572b3fb3cb8ba2a93cf30041ff3be133718d469145b710f2c94dc32",
+    ".cache/ford_c1_feedback/feedback_stress.json": "abf4e7bccc1e460008cc7450fcd92e9b2a6108bd71e53a01bdc24e31e5b5ad32",
+    ".cache/ford_c1_feedback/zero_error_stress.json": "2a3f284e10e5054205a788cce59bcf57bd837e13afff327457244141ba5522f0",
+    ".cache/ford_c1_feedback/safety_skip_reasons.txt": "5384c82b07b7cc20c6b22b8e94246cb104d53f8866af02b28fda7d4138cf377f"
+  },
+  "native_params": {
+    "library_sha256": "270bf43241cf7c02cc432cf78ec9411a62d7653ca445695efe785ae82241aa09",
+    "sources_match_original_rebuild_record": true,
+    "provenance": "Same locally rebuilt native library and source hashes recorded in ford_model_action_drive_test_validation.json; verified for this run."
+  },
+  "test_environment": {
+    "python": "/Users/ibpersonal/dev/sunnypilot/.venv/bin/python",
+    "PYTHONPATH": ".:opendbc_repo:.cache/ford_v6/test_deps",
+    "LOG_ROOT": "/private/tmp/ford-feedback-logs",
+    "PARAMS_ROOT": "/private/tmp/ford-feedback-params",
+    "PYTHONDONTWRITEBYTECODE": "1"
+  },
+  "source_sha256": {
+    "docs/ford_c1_feedback.md": "c1bc7f24c5ebe28679b4a04d09085d7b937926e63a38d43a3abfac93dfcfa0f9",
+    "docs/ford_model_action_candidate.md": "20cd8d10008cd796cc8719f5795ee80f50d8133d6e7684fb057a78fb05323fbe",
+    "docs/ford_model_action_drive_test.md": "7860ae26a61682aff86743ba302eb23c8f271d5700a2e616da1b6b38d438b57d",
+    "opendbc_repo/opendbc/car/vehicle_model.py": "ddc2a93d9c2b2ef6c9a913a5aef4c51e2bc387db1f7640473657e5ade4e50fac",
+    "openpilot/selfdrive/controls/controlsd.py": "2b7e246f00bccce3a2bb9f6f44009ca77690cadb8527cd2bdfe855e9ad72ad1e",
+    "openpilot/selfdrive/controls/lib/drive_helpers.py": "916bcd83c2a909a89795da58c7c43d7b168c9b82e1a6d281484bae45c667c01e",
+    "openpilot/selfdrive/controls/lib/ford_model_action.py": "4499defbb7fc5ddf5029ca42c549f0935b0758b08818c5bf0490fb52221f9a34",
+    "openpilot/selfdrive/controls/lib/ford_path.py": "383538fc7cdae3bc28dffb71fe12ac5f3f9866ffbe6adfb7457f3593e9fc903a",
+    "openpilot/selfdrive/controls/tests/test_ford_controlsd_logging.py": "84113b1b7800c868117af0034278f53a1a6153c7bb5fadc1ea45958e62c4f0d0",
+    "openpilot/selfdrive/controls/tests/test_ford_model_action.py": "cbe1b2aa1961deba3a42e1d82f5f75ae0c3d7a219428dea5f50cb70e1b27fd11",
+    "openpilot/selfdrive/controls/tests/test_ford_model_action_adapter.py": "c7f5ffd650e804e6e02fa12d435e0867b56b13a49c3d9fa511993188d5cb625a",
+    "openpilot/selfdrive/controls/tests/test_ford_model_action_feedback.py": "f7a956c082a246d9506e21adbf348cbdc7f94d5342d832841058c71f7e264eeb",
+    "openpilot/sunnypilot/selfdrive/controls/controlsd_ext.py": "7a13dc5ce49b40e27e05e62cdb9ef1bb764de8ed8167f7e982d54a4dffe97ed4",
+    "openpilot/sunnypilot/sunnylink/settings_ui.json": "7d38f315a7c5ce6d46d01a06f7eaddd4933f85639e5325ff71fdce22866ef401",
+    "openpilot/sunnypilot/sunnylink/settings_ui_src/pages/vehicle.yaml": "410e306958ece12e49fc114707741c57a2dd927c6ba3410e160834e52a759ea9",
+    "tools/ford_pscm_lab/feedback_replay.py": "ca552217953f3cce35da0b1666252fd43b6f8ab6c067e5c9102da8ea8d97c2f3",
+    "tools/ford_pscm_lab/model_action_replay.py": "af97c665f342c66b1be2502e188c63e6f3ee106d0a0d5e80997bc3040373ff9f",
+    "tools/ford_pscm_lab/stress_model_action.py": "0b25188edf2b248ebe741173ce02ce75bd59f1f39fd5bd909d41a3dca2294aa8"
+  },
+  "limitations": [
+    "Frozen route replay changes commands only; it cannot establish tracking, unwind response or closed-loop stability.",
+    "Measured curvature uses the existing steering-angle vehicle model; it is not an independent ground-path measurement.",
+    "No full device build, device boot, installation or road validation was performed."
+  ]
+}
@@ -0,0 +1,155 @@
+# Offline Ford selected-action candidate
+
+This document and `ford_model_action_validation.json` record the offline
+stage committed as `7ca3c6e3b`. The candidate is now available behind a
+separate default-off Sunnylink toggle; see
+[drive-test setup and validation](ford_model_action_drive_test.md).
+The counts, source hashes and selector status below describe that earlier
+stage. The current experiment adds [measured-curvature C1 feedback](ford_c1_feedback.md)
+to this original mapping; the historical no-feedback description below is
+not the current controller specification.
+
+The decision is `C0 = current model y(7 m)`,
+`C1 = max(7 m, speed × 1 s) × selected upstream-limited desiredCurvature`,
+with C2=C3=0. The 7 m station and one-second scale are engineering choices,
+not identified PSCM gains. `calibration_approved=false`.
+
+`openpilot/selfdrive/controls/lib/ford_model_action.py` contains the core
+and a separate adapter compatible with the existing controlsd call.
+At that stage, the production selector, v8 implementation, settings, opendbc
+submodule and Panda safety remained unchanged. Tests injected the adapter
+offline; there was no production setting. No hardware or CAN transmission
+occurs in the lab tools.
+
+## Construction and integration
+
+Only the unquantized C0 and C1 slew positions persist in the core.
+Each field is clipped independently (±5.11 m / ±0.5 rad), slewed independently
+(4 m/s / 0.5 rad/s), then packed using the existing Float32/sign-negation
+rounding contract (0.01 m / 0.0005 rad). Heading overflow is not transferred
+to C0. No yaw integral, blend, additional curvature contribution, reference
+filter, turn modes, or 10 m C1 cap is introduced.
+
+The selected standalone implementation from worktree 3548 is the provenance
+for this law. Its two-state packer has been moved into the library core so
+the controller does not depend on experimental lab code. Invalid numeric
+types, overflowing arc geometry and malformed paths reset the core instead
+of throwing or retaining a command.
+
+Arc stations use cumulative model x/y distance, not forward x. As in the
+reviewed standalone core, a path ending before 7 m holds its available
+endpoint instead of extrapolating. This matters: route95 contains 44 active
+cycles with 5.45–6.94 m of path at 2.78–3.46 m/s. A tested strict 7 m
+coverage gate would have introduced disengagements and was removed. There
+is no speed-dependent C0 horizon beyond this existing endpoint behavior.
+
+The adapter retains the existing input age allowance (−5 to +150 ms),
+speed domain (0.3–55 m/s), yaw sanity bound (±3 rad/s), selected curvature
+sanity bound (±1/m), and control interval (2–100 ms). It rejects backward
+model/measurement timestamps and invalid services. Repeated timestamps may
+continue slew, but geometry is validated again on each tick. Disengagement,
+invalid inputs and timing faults clear all command and adapter timing state.
+The first valid tick after reset uses 10 ms, as v8 does.
+
+controlsd still owns reference selection, upstream curvature limiting,
+service health and engagement. Tests execute its actual source-selection
+and limiter code, its Ford call, Float32 publication in ControlsExt, conversion
+to CarControlSP, and the pinned Ford CarController's in-memory CAN builder.
+Both model-action and maneuver-planner selection are covered, including
+disabling latActive after invalid output. Only the test chooses the adapter.
+
+Yaw is not an input to the control law. The adapter checks it solely for the
+inherited invalid-input policy. Driver override and optional PSCM status
+do not modify the candidate base; existing engagement and downstream driver
+arbitration remain responsible for authorization, as with v8's base request.
+
+## Offline evidence
+
+The checked-in `ford_model_action_validation.json` records the completed
+checks and source hashes. Full arrays and detailed reports are generated
+locally under `.cache/ford_model_action/`; original route files are read-only.
+
+Completed validation: **264 Ford tests and 150 subtests pass**, including
+120 new core/adapter/replay-validator cases. The candidate module has 100%
+statement and branch coverage (78 statements, 24 branches). Ruff and Ty pass.
+The 200,000-cycle numerical stress test also checks 200,000 mirrored core
+updates and 18,138 field-boundary cases. Across route and stress runs,
+485,238 Float32/CAN round trips pass. Eight deliberately injected faults
+(heading gain/cap, erased C0, wrong C0 slew, retained invalid state, stale
+model acceptance, model clock rollback and reversed C0 sign) are all caught
+by the tests. Mutation runs replace code only inside isolated Python
+processes; production source files are never modified by those probes.
+
+Independent Standards and Spec reviews reported zero findings. The full
+suite's Params setting test uses an existing local native library from
+worktree 3548 after checking relevant source files are byte-identical;
+its hash and provenance are in the manifest. That library is an ignored
+test dependency, not part of this change. This is the full relevant Ford
+suite, not the hardware-dependent test suite for every openpilot subsystem.
+
+The replay has two separate passes:
+
+* Core compatibility uses the archived eligibility mask and requires exact
+  equality with the independently implemented `action_heading` commands.
+* Adapter reconstruction derives eligibility from recorded service streams
+  independently of the archived output mask. It retains original timestamps,
+  gaps and consumed model frames. Controls publication time proxies the
+  unlogged computation clock, and complete SubMaster health is unavailable.
+
+All 54,738 route95 and 78,812 route90 core cycles match exactly, including
+37,614 and 73,055 active cycles. The adapter preserves those active counts.
+Its 59 / 19 changed commands arise solely from the fresh 10 ms engagement
+tick instead of the archived harness's preceding publication interval;
+the replay checks that attribution on every cycle. Maximum differences are
+0.01 m / 0.001 rad (95) and 0.02 m / 0.002 rad (90).
+
+Every core and adapter replay output is round-tripped through Float32 and
+the real CAN packer/parser, including zero C2/C3, signs, mode and counter.
+Continuous field slew and quantization allowance are checked separately
+from immediate invalid-command resets. The original driver-clean cohorts,
+speed strata and command RMS are reproduced without redoing the encoder search.
+
+The numerical stress harness uses analytic rotated paths, scalar slew
+arithmetic, mirrored requests, irregular intervals and invalid-input resets.
+It also sweeps every representable host field value and the Float32 values
+immediately below, at and above every half-quantum boundary. Direct CAN
+packing of the continuous state must agree with the host's quantized output.
+The unit tests cover releases, reversals, clipping, service freshness,
+clock resets, malformed inputs, endpoint fallback and actual integration.
+
+## Limits of the result
+
+On turns at ≥15 m/s, candidate C0 RMS is 79%/81% below v8 on routes95/90,
+while C1 is 33%/41% higher. Those are command changes, not evidence of
+equivalent steering authority. The PSCM's independent C0/C1 response remains
+unknown. Replay cannot establish physical model following, strong turns,
+centering, overshoot, oscillation or closed-loop stability.
+
+The release probe is intentionally explicit: a model bend can increase
+while selected curvature decreases. At 20 m/s, one synthetic probe changes
+C0/C1 from 0.24 m / 0.10 rad to 0.49 m / 0.08 rad. Zero selected curvature
+sets the C1 target to zero but does not erase a nonzero current model C0.
+Removing a yaw-integral tail does not prove that physical overshoot is solved.
+No additional release policy or unsupported plant model is added to hide
+that uncertainty.
+
+## Reproduce
+
+From this worktree, use the logged construction dependency explicitly:
+
+```sh
+export PYTHONDONTWRITEBYTECODE=1
+export PYTHONPATH=.:/Users/ibpersonal/.codex/worktrees/b926/sunnypilot/opendbc_repo
+PY=/Users/ibpersonal/dev/sunnypilot/.venv/bin/python
+EVIDENCE=/Users/ibpersonal/.codex/worktrees/3548/sunnypilot/analysis/controller_search_20260904
+$PY -m pytest -q -p no:cacheprovider openpilot/selfdrive/controls/tests/test_ford_*.py tools/ford_pscm_lab openpilot/selfdrive/car/tests/test_ford_pscm_status.py
+$PY -m tools.ford_pscm_lab.model_action_replay "$EVIDENCE/route95" --output .cache/ford_model_action/route95
+$PY -m tools.ford_pscm_lab.model_action_replay "$EVIDENCE/route90" --output .cache/ford_model_action/route90
+$PY -m tools.ford_pscm_lab.stress_model_action --cycles 200000 --seed 20260907 --output .cache/ford_model_action/stress.json
+```
+
+The route replay refuses an opendbc revision other than
+`72a775d35e54c21ff5c5798acef22016eedcc0a7`. Stress defaults to this pin and
+also accepts an explicitly required commit with `--opendbc-revision` for
+deployment checks. A mismatch still fails. This historical pin reproduces
+logged construction; it does not change the merge's submodule pointer.
@@ -0,0 +1,53 @@
+# Ford selected-action drive-test branch
+
+The current experiment adds [measured-curvature C1 feedback](ford_c1_feedback.md)
+to the restored original v1 mapping. It is selectable on the **Ford CAN FD
+F-150 Lightning** through the existing persistent, default-off Sunnylink
+toggle. Offline checks establish software behavior; physical tracking,
+turn-exit behavior and closed-loop stability remain unvalidated.
+
+## Select and restore
+
+1. Install branch `hiimisaac-dev` from `sunnypilot/sunnypilot` using the device's
+   normal branch-switch process and allow its build to finish.
+2. While offroad, open Sunnylink device settings → Vehicle → Ford and enable
+   **Selected-Action Path Tracking (Experimental)** (`FordModelActionController`).
+3. Complete a real offroad-to-onroad cycle. Selection occurs when `controlsd`
+   starts; a stored toggle change or disengagement alone cannot swap an active
+   controller. Initial physical evaluation remains controlled testing.
+
+The startup event `Ford path controller selected` should report
+`FordModelActionController`. Periodic `Ford C2-free path tracking` events
+identify **`hypothesis=model-action-c1-feedback-v1`**. They report desired and
+measured curvature, base heading, accumulated correction, applied heading,
+feedback timing and driver/PSCM gating. `calibration_approved=false` remains.
+
+Turning the toggle off and completing another offroad-to-onroad cycle restores
+**PSCM Coefficient Observer** if selected, otherwise the original Ford path
+controller. The stored observer selection is preserved. The experiment takes
+priority on the supported vehicle; other vehicles retain their existing
+controller. A leftover `FordVirtualAngleController` parameter has no effect.
+
+## Wiring and validation
+
+`controlsd` supplies the selected, upstream-limited desired curvature and the
+measured steering-derived curvature already used in its tracking diagnostics.
+Fresh steering publications advance C1 feedback. Repeated publications may
+advance output slew but cannot integrate the same elapsed interval twice.
+Driver override clears the correction. A fresh PSCM reached-limit flag stops
+extra outward accumulation while preserving unwind and base model changes.
+
+C0 retains the original 7 m model-path mapping. C2 and C3 remain zero. The
+existing output limits, 100 Hz sender, Float32 publication and CAN builder
+remain in place. No opendbc submodule or Panda safety change is required.
+
+See [the feedback specification and validation](ford_c1_feedback.md) and
+`ford_c1_feedback_validation.json` for the current evidence and reproduction
+commands. `ford_model_action_validation.json` and
+`ford_model_action_drive_test_validation.json` are historical records for the
+original offline candidate and its first wiring, respectively; their counts
+and coverage are not claims about the feedback version.
+
+The full hardware build and device boot are not performed by these offline
+checks. Pushing the branch does not install it on the device or change its
+stored toggle.
@@ -0,0 +1,145 @@
+{
+  "date": "2026-09-07",
+  "baseline_commit": "7ca3c6e3b3e659c6f446039501c5826bbd14092e",
+  "branch": "codex/ford-model-action-drive-test",
+  "scope": "Default-off Sunnylink selection and v8 retirement; offline validation only. No device installation or physical performance validation.",
+  "calibration_approved": false,
+  "production_selector_changed": true,
+  "toggle": "FordModelActionController",
+  "default_enabled": false,
+  "v8_removed": true,
+  "panda_safety_changed": false,
+  "opendbc_submodule_changed": false,
+  "deployment_opendbc_head": "c21a9013700734dd20b09e05aa68329ad8cc20f9",
+  "controller_size": {
+    "total_lines": 145,
+    "code_lines_excluding_blanks_comments_docstrings": 95,
+    "core_persistent_values": 2,
+    "adapter_timestamps": 3,
+    "removed_v8_module_lines": 469
+  },
+  "tests": {
+    "combined_ford_params_sunnylink_suite": "284 passed, 26 subtests passed in 2.63s",
+    "suite_log_sha256": "2e223a507f0630481cf6f83b9f8893d226f3f4273a79a09fc35905aa875b1d2c",
+    "coverage": {
+      "covered_lines": 87,
+      "num_statements": 87,
+      "percent_covered": 100.0,
+      "percent_covered_display": "100",
+      "missing_lines": 0,
+      "excluded_lines": 0,
+      "percent_statements_covered": 100.0,
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+}
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+}
@@ -0,0 +1,327 @@
+# Ford C2-free model-pose tracking with measured feedback
+
+This experiment is retired. Its implementation, setting and dedicated tests
+were removed from the selected-action drive-test branch. For current setup,
+see [Ford selected-action drive testing](ford_model_action_drive_test.md).
+The material below is historical; it does not describe an available toggle.
+
+Hypothesis `model-pose-c0-c1-feedback-v8` retains the model-pose C0/C1 base
+and adds two guarded release policies. When measured turning exceeds both
+current and delayed requests, a separate output guard prevents same-direction
+C0/C1 growth, including while feedback history rebuilds after driver input.
+When turning instead falls below both requests and is no longer increasing,
+bounded C1 tracking can use remaining release-entry command headroom.
+Existing opposing-bias recovery still stops at zero bias. Geometry, blending,
+feedback gain, slew rates and field limits are unchanged; C2/C3 remain zero.
+
+This is an experimental outer controller around the multivariable PSCM.
+Its geometry does not define a calibrated C0/C1-to-wheel mapping or an angle
+servo. V8 has offline validation only. Command replay cannot establish the
+truck's response, closed-loop stability, or an overshoot improvement.
+
+## Evidence and scope
+
+Route80 ran v3 and contains both sustained under-response and over-response.
+Representative eligible windows had median CAN response/request ratios of
+0.78, 1.77 and 0.69 with a declared 0.2-second comparison interval. These
+are descriptive tracking ratios, not identified controller gains.
+
+V4 replaced separate model-heading C1 with selected-curvature C1 and reduced
+heading demand in several large maneuvers. The user subsequently reported
+weak turning and steering repeatedly stopping near 85 degrees. Older logs
+contain larger wheel angles; the inspected host code has no fixed 85-degree
+wheel stop, although upstream curvature limits depend on speed.
+
+Route83 had the Sunnylink toggle on, but omitted EPS firmware responses.
+The former firmware gate selected the default `FordPathController`; replay
+reproduced its recorded C0/C1/C2 requests. Its favorable turns are evidence
+for the existing model-pose construction, not validation of v5 or v6.
+V6 reuses that construction while replacing its remaining C2 request with
+C0/C1 geometry. Removing C2 changes the request received by the PSCM, so
+matching large C0/C1 commands does not guarantee matching vehicle motion.
+
+Route8a ran v6 and was reported as the best drive. Route8e ran v7 throughout
+with the experiment enabled; it includes entry lag and excessive turning
+while requests release. Fixed-input v6/v7 replay produced identical commands
+in the main reversal and over-response examples, so the v7 recovery change
+does not directly explain their command behavior. In the over-response
+example, model C0/C1 grew while selected curvature fell and driver resets
+repeatedly removed feedback history. Another exit remained deficient after
+opposing bias reached zero. These observations motivate the v8 guards; they
+do not isolate an EPS transfer function or demonstrate the proposed response.
+
+## Base request
+
+controlsd selects valid `lateralManeuverPlan.desiredCurvature`, otherwise
+`modelV2.action.desiredCurvature`, after the existing curvature limiter.
+This action already includes upstream delay handling; it receives no extra
+response advance here.
+
+The model contribution uses the existing allocator's raw forward pose and
+bounded short-pose correction. `_model_pose` advances 0.1 seconds, retains
+the model's remaining forward geometry, and separately corrects the short
+pose using measured curvature and its recent change. Its offset preview is
+up to 7 m and its heading preview is up to max(7 m, speed × 1 s), bounded by
+available path length. This raw pose is not passed through a second model
+filter. The filtered, ego-aligned reference remains available for comparison
+and the existing geometry-validity checks.
+
+```text
+share(k) = clip((k - 0.006/m) / (0.012/m - 0.006/m), 0, 1)
+aligned = desired_curvature × model_forward_heading > 0
+model_share = min(share(abs(desired_curvature)), share(model_curvature_demand))
+              if aligned, otherwise 0
+model_pair = existing_pose_encoder(model_pose, model_share, C2=0)
+
+remaining_curvature = desired_curvature × (1 - model_share)
+L0 = max(8 m, speed × 1 s)
+L1 = max(7 m, speed × 1 s)
+curvature_C0 = 0.5 × remaining_curvature × L0²
+curvature_C1 = remaining_curvature × L1
+C0_base = clip(model_pair.C0 + curvature_C0, ±5.11 m)
+C1_base = clip(model_pair.C1 + curvature_C1, ±0.5 rad)
+```
+
+`model_curvature_demand` is the larger absolute curvature implied by the
+forward offset and heading previews. The share uses the existing allocator's
+0.006–0.012/m thresholds. Both model and action must request a substantial
+turn in the same direction before model pose supplies the full base.
+Small, flat, opposed or zero requests use the curvature contribution; zero
+action produces a zero base. Partial shares combine both contributions.
+The existing pose encoder retains its quantization and field-allocation rules.
+The residual-curvature lift is geometric, not a claim of EPS equivalence to C2.
+
+The inherited pose encoder allocates heading overflow using its asymmetric
+limits (+0.5235/−0.5 rad), before the symmetric final ±0.5 rad
+heading bound. On clipped tails, this can leave mirrored C0 requests differing
+by up to 0.0235 rad × 7 m = 0.1645 m. The favorable comparison anchors lie
+below that heading cap; full model-base odd symmetry is not claimed.
+
+## Measured feedback and limits
+
+```text
+past_request = selected curvature held at or before (measurement_time - delay)
+yaw_error = measured_speed × past_request - measured_yaw_rate
+bias_trial = released_bias + feedback_gain × yaw_error × measurement_dt
+C1_unconstrained = clip(C1_base + accepted_bias, ±0.5 rad)
+C1_target = temporary_backoff_ceiling(C1_unconstrained) if backoff_active
+            otherwise C1_unconstrained
+```
+
+Measured yaw is negated Ford CAN yaw, matching the control sign convention.
+The historical request uses zero-order hold; it never interpolates toward a
+future publication. Nominal comparison delay is `CP.steerActuatorDelay`
+(0.2 seconds on the source vehicle). Feedback compares against selected
+curvature, not curvature inferred from the model-pose coefficients.
+
+| Quantity | Value |
+|---|---:|
+| C0 / C1 final bounds | ±5.11 m / ±0.5 rad |
+| Independent C0 / C1 slew | 4 m/s / 0.5 rad/s |
+| Feedback integration scale | 1.0 |
+| Feedback minimum speed | 2 m/s |
+| Maximum PSCM/core input age | 150 ms |
+| Allowed timestamp lead | 5 ms |
+| Release comparison tolerance | one C1 wire quantum, 0.0005 rad |
+
+The integration scale, preview distances and blend thresholds are effective
+gains; none establishes stability. No wheel-response gain is fitted.
+Zero yaw error retains acquired bias while an eligible turn continues.
+Host anti-windup admits reachable correction within the combined C1 field
+and slew limits. Feedback overflow is not transferred into C0.
+
+The release logic scales bias as the bounded base decreases and resets on
+zero/reversal. When delayed curvature still represents a stronger or opposing
+request, or PSCM reports LimitReached, new integration is normally frozen.
+One exception permits measured-error backoff: measured turning must exceed
+both the delayed and current selected yaw requests in the base's direction,
+and total heading must still have the base's sign. Exceeding only an older,
+smaller request during turn-in does not qualify. The accepted increment may
+only reduce that existing total toward zero; it cannot grow the request or
+carry it through zero. Existing host field and slew limits still apply.
+
+The existing release-recovery exception requires fresh valid PSCM status with
+limit below 2, retained bias opposing the base, and both current and delayed
+requests aligned with that base. Measured turning must be below both requests
+in their direction. It then uses the current yaw deficit × the existing
+feedback gain × measurement interval to unwind only the opposing bias toward
+zero. The increment is clipped so recovery cannot cross zero bias or create
+demand beyond the existing base. Common host anti-windup still limits what
+can be accepted. A separate release-tracking exception is described below;
+other constrained cases remain frozen. PSCM limit 2 never permits either
+request-increasing exception.
+The no-new-bias restriction applies to `release_recovery`. It does not apply
+to the separate bounded `release_tracking` branch. Once release ends,
+ordinary eligible integration can add correction beyond the base as before;
+its existing limits and guards are unchanged.
+
+`release_recovery` and `feedback_recovery_active=true` indicate that the
+recovery branch actually changed bias on that update. If host anti-windup
+blocks the entire increment, the status remains `host_limit` and the flag is
+false. Recovery is evaluated only on fresh measurements; the flag is false
+on repeated-measurement updates and after reset.
+
+Diagnostics distinguish `release_backoff` and `pscm_backoff`; a release takes
+precedence when both conditions apply. While `feedback_backoff_active` is
+true, total C1 is also capped at the preceding continuous heading request in
+the current request direction and at zero in the opposite direction. This
+ceiling affects the output only: it is not stored or projected into bias.
+The measured-error increment can still update bias under the normal limits,
+but a changing model base does not create persistent integral suppression.
+The ceiling persists between repeated measurements; C1 cannot grow or reverse
+while it applies. The next fresh measurement clears it unless backoff is
+again warranted. It does not cap C0, and normal feedback has its own rules
+outside backoff. Independent slew remains 0.5 rad/s for C1 and 4 m/s for C0.
+Backoff still compares against the delayed reference, so response lag remains.
+Reducing a request does not demonstrate that physical overshoot is resolved.
+
+## V8 release guard and tracking
+
+`ReleaseGuard` retains selected-request history independently of feedback
+bias history. Driver-related feedback resets do not erase that reference,
+but the guard still requires current fresh valid PSCM status, no current
+driver override, and the existing input and speed eligibility. Invalid core
+input or disengagement resets its history with the controller.
+
+During release, measured yaw must exceed both the current and delay-matched
+requests in the requested turn direction. Only then does the guard cap
+same-direction C0/C1 growth at each preceding continuous request. Terms
+already reducing the turn, including an opposing C0 centering offset, remain
+available. The guard follows base allocation and C1 feedback, so changing
+model geometry cannot bypass it. Its ceilings affect outputs, never stored
+bias. No scalar-curvature cap replaces strong model geometry during turn-in
+or undertracking. Existing independent slew and field limits still apply.
+
+`release_tracking` addresses an eligible release deficit once bias is zero
+or already in the base's direction. Both current and delayed requests must
+align with that base, measured turning must be below both, and measured
+curvature must not be rising in the turn direction across the response
+interval by more than one C1 wire quantum after scaling by heading preview.
+Fresh valid PSCM status with limit below 2 is required. The current yaw deficit
+uses the existing integration gain and measurement interval;
+new C1 tracking increments are limited by command headroom captured at
+release entry, tapered with remaining desired curvature. The allowance is
+`max(0, entry_command_magnitude - abs(base)) × min(1, abs(desired) / entry_reference)`
+above the current base; any existing same-direction bias consumes it first.
+This limits new tracking integration, not the existing model base or bias.
+Only that additional allowance is tapered; strong model geometry remains
+available. A brief pause does not reacquire a higher entry
+ceiling; a full response interval without release ends the retained episode.
+Common host anti-windup, field and slew bounds still apply. Opposing bias
+continues through `release_recovery`, which stops at zero, before any separate
+tracking exception can be considered.
+
+Neither exception relaxes the PSCM LimitReached growth restriction. The
+reference delay and finite response time remain; these output policies are
+command-construction changes, not evidence of improved physical tracking.
+
+## PSCM status and driver handling
+
+card publishes `Lane_Assist_Data3_FD1` in `carStateSP.fordPscmStatus`, retaining
+the original CAN receipt timestamp. Republishing carStateSP or receiving
+unrelated frames cannot refresh it. The opendbc submodule is unchanged.
+
+Feedback requires valid fresh status, InProgress lateral state (2), capability
+LimitedModeAvailable or ExtendedModeAvailable (1 or 2), and no denial.
+Missing, malformed, stale, backward-timestamped, denied or unavailable status
+clears feedback bias/history and disables the separate release guard,
+leaving the base subject to its core validity gates.
+LimitReached (2) permits only the bounded request-reducing backoff described
+above and otherwise freezes integration. LimitWithDriverActive (3) clears
+feedback. Backoff still requires fresh, valid, InProgress status with an
+available capability and no denial. These generic PSCM reports do not identify
+a specific torque or rate limit.
+
+`steeringPressed`, raw torque above the existing Ford driver allowance, or
+nonfinite torque clear feedback. Below 2 m/s feedback also clears. A fresh
+feedback reference interval is required after override; the independent
+release guard can use retained valid request history once its current gates
+are satisfied. Base requests retain normal
+PSCM driver arbitration while lateral control remains authorized; an unset
+override flag cannot rule out subthreshold driver influence.
+
+## Gates and Sunnylink selection
+
+Core model/action/car-state freshness, finite-value, clock and speed checks
+remain in place. Invalid core inputs reset both commands and clear latActive.
+Raw model geometry is validated on every update, including repeated model
+timestamps; an invalid raw path cannot reuse the cached valid reference.
+Missing PSCM status disables feedback, not an otherwise valid base request.
+
+Vehicle → Ford → **C2-Free Path Tracking (Experimental)** retains the
+`FordVirtualAngleController` key, default-off setting and offroad/onroad cycle
+requirement. Enabled selects v8 on Ford CAN FD `FORD_F_150_LIGHTNING_MK1`
+regardless of missing or different EPS firmware-query results. Other platforms
+retain their existing controller. V8 takes priority over PSCM Coefficient
+Observer while selected; disabling and cycling offroad/onroad restores the
+previous selection. Controller selection does not force lateral engagement.
+
+The analyzed firmware is `RL38-14D003-AA`; removing the eligibility check
+is not validation of other firmware. No live device setting is changed.
+
+## Diagnostics and verification
+
+The 5 Hz `Ford C2-free path tracking` event keeps its name and identifies v8.
+`model_offset_base` / `model_heading_base` report the already weighted and
+encoded model contribution; `curvature_offset_base` / `curvature_heading_base`
+report the residual-curvature contribution. `model_share` and `base_guard`
+identify model-pose, blended, curvature-only, opposed-model and zero-request
+cases. `heading_base` is the bounded pre-feedback C1. `offset_target` and
+`heading_target` are the final targets after the independent release guard;
+`offset_target_unguarded` and `heading_target_unguarded` retain the inputs to
+that guard. The latter C1 already includes its normal feedback/backoff policy.
+
+The event retains source timestamps, measured curvature/yaw, final commands,
+slew scales, feedback bias/status/history, raw torque and PSCM status/age.
+`feedback_backoff_active` records the persistent heading ceiling, including
+cycles whose feedback status is `no_new_measurement`.
+`release_guard_active` and `release_guard_reference_curvature` expose the
+independent C0/C1 guard and its retained delayed reference.
+`feedback_release_tracking_active`, `feedback_release_ceiling` and
+`feedback_curvature_delta` identify accepted release
+tracking, the total-heading threshold used to admit new bias, and the
+measured-curvature change across the response interval (1/m). The tracking
+flag is true only when the branch accepts a bias change on a new measurement;
+it is false on repeated measurements. The ceiling/trend fields can describe
+an evaluated condition even when no increment is accepted.
+`feedback_recovery_active` records an accepted recovery increment on this
+update only; it does not persist between measurements.
+`feedback_yaw_error` retains its delayed-reference meaning. Recovery instead
+uses current error, reconstructed from logged `desired_curvature`,
+synchronized car-state speed and `yaw_rate`; those two errors can differ.
+During backoff or the independent release guard, `heading_target` can be lower in the request direction than
+the bounded sum of `heading_base` and `heading_bias`, because the temporary
+ceiling is not part of the stored bias.
+`model_heading_target` remains a filtered comparison reference; it is not the
+weighted model contribution. `angleState.saturated` is not an EPS-limit signal.
+
+Validation must cover large recorded maneuvers, flat-model centering, both
+turn directions, model/action disagreement, share transitions, release and
+reversal, release/limit backoff without growth or zero crossing, status/driver
+resets, reference causality, bounds, slew and CAN packing with C2/C3 zero.
+Recovery checks cover both directions, stopping at zero bias, repeated
+measurements, current-and-delayed agreement, and rejection at PSCM limit 2.
+Old v3/v4 command-equality expectations do not define
+v8 success. Guard checks also cover driver reset/history rebuilding,
+same-direction growth, opposing coefficients, repeated measurements,
+undertracking and invalid-status inhibition. Tracking checks cover delayed
+curvature trends and tapered release-entry headroom. Historical v5–v7 replay
+results remain historical observations.
+
+The v8 recorded-input fixture contains 15,273 cycles with 4,879 selected
+evidence samples. Base allocation and output eligibility match v7. In the
+clean deficient exit, median absolute C1 changes from 0.0665 to 0.0845 rad
+while C0 stays unchanged. The growth guard also acts while feedback history
+rebuilds; the largest over-growth witness includes nearby driver input and
+is excluded from the strict autonomous tracking score. Both good comparison
+curves in that fixture retain their median requests, and the older large-turn
+fixtures retain their required command scale.
+
+On the earlier good drive, one comparison curve retains extra C1 after
+eligible release tracking: median magnitude changes from 0.121 to 0.128 rad.
+In its 103–110 s interval, tracking increments occur only while measured
+turning falls short, with a median current response/request ratio of 0.895.
+Acquired bias can persist after matching, as with ordinary integral feedback.
+This collateral command change remains a reason to compare new vehicle logs.
+Replay fixes recorded motion and planner outputs, so enabled vehicle logs
+are still required to assess tracking error, oscillation and interventions.
@@ -24,7 +24,9 @@ function agnos_init {
    if $AGNOS_PY --verify $MANIFEST; then
      sudo reboot
    fi
-    $DIR/openpilot/common/hardware/comma/updater $AGNOS_PY $MANIFEST
+    while true; do
+      $DIR/openpilot/common/hardware/comma/updater $AGNOS_PY $MANIFEST
+    done
  fi
 }

@@ -16,7 +16,7 @@ export VECLIB_MAXIMUM_THREADS=1
 export QCOM_PRIORITY=12

 if [ -z "$AGNOS_VERSION" ]; then
-  export AGNOS_VERSION="19.6"
+  export AGNOS_VERSION="19.7"
 fi

 export STAGING_ROOT="/data/safe_staging"
@@ -131,6 +131,7 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
    downloaded @2;
    cached @3;
    failed @4;
+    verifying @5;
  }

  struct DownloadProgress {
@@ -352,6 +353,7 @@ struct OnroadEventSP @0xda96579883444c35 {
    speedLimitPending @22;
    e2eChime @23;
    laneChangeRoadEdge @24;
+    bigModelReady @25;
  }
 }

@@ -381,6 +383,7 @@ struct CarControlSP @0xa5cd762cd951a455 {
  leadOne @2 :LeadData;
  leadTwo @3 :LeadData;
  intelligentCruiseButtonManagement @4 :IntelligentCruiseButtonManagement;
+  fordLateralPath @5 :FordLateralPath;

  struct Param {
    key @0 :Text;
@@ -401,6 +404,14 @@ struct CarControlSP @0xa5cd762cd951a455 {
  }
 }

+struct FordLateralPath {
+  pathOffset @0 :Float32;     # c0 [m]
+  pathAngle @1 :Float32;      # c1 [rad]
+  curvature @2 :Float32;      # c2 [1/m]
+  curvatureRate @3 :Float32;  # c3 [1/m^2]
+  valid @4 :Bool;
+}
+
 struct BackupManagerSP @0xf98d843bfd7004a3 {
  backupStatus @0 :Status;
  restoreStatus @1 :Status;
@@ -445,6 +456,16 @@ struct BackupManagerSP @0xf98d843bfd7004a3 {

 struct CarStateSP @0xb86e6369214c01c8 {
  speedLimit @0 :Float32;
+  fordPscmStatus @1 :FordPscmStatus;
+
+  struct FordPscmStatus {
+    valid @0 :Bool;
+    canMonoTime @1 :UInt64;  # Last accepted Lane_Assist_Data3_FD1 CAN receipt, not carStateSP publication time.
+    lateralState @2 :UInt8;  # LatCtlSte_D_Stat
+    limit @3 :UInt8;  # LatCtlLim_D_Stat: generic lateral limit, not a torque/rate diagnosis.
+    capability @4 :UInt8;  # LatCtlCpblty_D_Stat
+    denied @5 :Bool;  # LaActDeny_B_Actl
+  }
 }

 struct LiveMapDataSP @0xf416ec09499d9d19 {
@@ -468,7 +489,30 @@ struct ModelDataV2SP @0xa1680744031fdb2d {
  }
 }

-struct CustomReserved10 @0xcb9fd56c7057593a {
+struct AssistedDrivingMilestoneState @0xcb9fd56c7057593a {
+  enabled @0 :Bool;
+  madsDistanceMeters @1 :Float64;
+  fullAssistDistanceMeters @2 :Float64;
+  event @3 :Event;
+
+  struct Event {
+    id @0 :UInt64;
+    category @1 :Category;
+    distanceMeters @2 :Float64;
+    previousDistanceMeters @3 :Float64;
+    unit @4 :Unit;
+  }
+
+  enum Category {
+    none @0;
+    mads @1;
+    fullAssist @2;
+  }
+
+  enum Unit {
+    imperial @0;
+    metric @1;
+  }
 }

 struct CustomReserved11 @0xc2243c65e0340384 {
@@ -725,6 +725,7 @@ struct ChestnutState {
  pcieLtssm @7 :UInt8;
  supplyVoltage @8 :UInt16;  # mV
  supplyCurrent @9 :Int16;  # mA
+  supplyFault @10 :Bool;
 }

 struct RadarState @0x9a185389d6fdd05f {
@@ -1004,6 +1005,7 @@ struct DrivingModelData {
  frameIdExtra @1 :UInt32;
  frameDropPerc @6 :Float32;
  modelExecutionTime @7 :Float32;
+  big @8 :Bool;

  action @2 :ModelDataV2.Action;

@@ -2640,7 +2642,7 @@ struct Event {
    carStateSP @114 :Custom.CarStateSP;
    liveMapDataSP @115 :Custom.LiveMapDataSP;
    modelDataV2SP @116 :Custom.ModelDataV2SP;
-    customReserved10 @136 :Custom.CustomReserved10;
+    assistedDrivingMilestoneState @136 :Custom.AssistedDrivingMilestoneState;
    customReserved11 @137 :Custom.CustomReserved11;
    customReserved12 @138 :Custom.CustomReserved12;
    customReserved13 @139 :Custom.CustomReserved13;
@@ -90,6 +90,7 @@ _services: dict[str, tuple] = {
  "carParamsSP": (True, 0.02, 1),
  "carControlSP": (True, 100., 10),
  "carStateSP": (True, 100., 10),
+  "assistedDrivingMilestoneState": (True, 10., 1),
  "liveMapDataSP": (True, 1., 1),
  "modelDataV2SP": (True, 20., None, QueueSize.BIG),
  "liveLocationKalman": (True, 20.),
@@ -56,29 +56,29 @@
  },
  {
    "name": "boot",
-    "url": "https://commadist.azureedge.net/agnosupdate/boot-b30f5eef65ec3878f3aa3dcaf2cc95c09e2c1e661cd3a38e94da37dee76f68bd.img.xz",
-    "hash": "b30f5eef65ec3878f3aa3dcaf2cc95c09e2c1e661cd3a38e94da37dee76f68bd",
-    "hash_raw": "b30f5eef65ec3878f3aa3dcaf2cc95c09e2c1e661cd3a38e94da37dee76f68bd",
+    "url": "https://commadist.azureedge.net/agnosupdate/boot-6ecf6f987cd11968104abcccabbe268485d329cdb73012dfd3c381a6b8deb27d.img.xz",
+    "hash": "6ecf6f987cd11968104abcccabbe268485d329cdb73012dfd3c381a6b8deb27d",
+    "hash_raw": "6ecf6f987cd11968104abcccabbe268485d329cdb73012dfd3c381a6b8deb27d",
    "size": 46897152,
    "sparse": false,
    "full_check": true,
    "has_ab": true,
-    "ondevice_hash": "6650e4c46df99ae6dfd6ee895a34b8a2a3cc490a8ce18e16cc3c451c3f822b6e"
+    "ondevice_hash": "d12e1e5b9455b62a1464558716493b33e470d7a7e88da1c4105a3b21d0961808"
  },
  {
    "name": "system",
-    "url": "https://commadist.azureedge.net/agnosupdate/system-5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3.img.xz",
-    "hash": "b134fd04e9da27fa1d359ea0f2742c216fa21a08b5c47e9be22ab3b0563d9b9b",
-    "hash_raw": "5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3",
+    "url": "https://commadist.azureedge.net/agnosupdate/system-3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f.img.xz",
+    "hash": "74ffc9c551e1f29cda897ace8a69080fe644f8039977c6885f2b48362e39b744",
+    "hash_raw": "3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f",
    "size": 4718592000,
    "sparse": true,
    "full_check": false,
    "has_ab": true,
-    "ondevice_hash": "91242772af771ae96fe2eebc105f2b80a7e1dbaaf6003c2574b62d51b806f468",
+    "ondevice_hash": "6a992680183685eea9db99d915219a37935f45989330d9b619e880450257f448",
    "alt": {
-      "hash": "5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3",
-      "url": "https://commadist.azureedge.net/agnosupdate/system-5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3.img",
+      "hash": "3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f",
+      "url": "https://commadist.azureedge.net/agnosupdate/system-3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f.img",
      "size": 4718592000
    }
  }
-]
+]
@@ -5,6 +5,7 @@ import logging
 import os
 import select
 import signal
+import string
 import struct
 import subprocess
 import tempfile
@@ -354,7 +355,7 @@ class Modem:
      imei = ""

    iccid = (self._atv("AT+QCCID", "+QCCID:") or "").rstrip("F")
-    if not iccid.isdigit():
+    if not all(c in string.hexdigits for c in iccid):
      iccid = ""

    imsi = first_line("AT+CIMI")
@@ -4,11 +4,17 @@ from pathlib import Path
 CHESTNUT_FW_VERSION = "ed4e39b7"
 CHESTNUT_USB_IDS = ((0xADD1, 0x0001), (0x3801, 0x0001))
 CHESTNUT_ROM_USB_IDS = ((0x174C, 0x2464), (0x174C, 0x2463))
+CHESTNUT_USB_PRODUCT = f"custom {CHESTNUT_FW_VERSION}-CLEAN"
 USB_DEVICES_PATH = Path("/sys/bus/usb/devices")
 TYPEC_CC_ORIENTATION_PATH = Path("/sys/class/power_supply/usb/typec_cc_orientation")
 PRIMARY_USB_CONTROLLER = "a600000.ssusb"


+def is_chestnut_usb_id(vendor_id: int, product_id: int, include_bootloader: bool = False) -> bool:
+  ids = CHESTNUT_USB_IDS + CHESTNUT_ROM_USB_IDS if include_bootloader else CHESTNUT_USB_IDS
+  return (vendor_id, product_id) in ids
+
+
 def get_usb_topology() -> set[str]:
  try:
    return set(os.listdir(USB_DEVICES_PATH))
@@ -81,7 +87,7 @@ def set_usb_state(device_state, devices: list[dict]) -> None:
    entry.linkErrorCount = device["linkErrorCount"]
    entry.usb3Lane = device.get("usb3Lane", "unknown")

-    if (entry.vendorId, entry.productId) in CHESTNUT_USB_IDS:
+    if is_chestnut_usb_id(entry.vendorId, entry.productId):
      chestnut_present = True

  device_state.chestnutPresent = chestnut_present
@@ -97,6 +97,10 @@ Params::Params(const std::string &path) {
 }

 Params::~Params() {
+  flushNonBlockingWrites();
+}
+
+void Params::flushNonBlockingWrites() {
  if (future.valid()) {
    future.wait();
  }
@@ -75,6 +75,7 @@ public:
    return put(key.c_str(), val ? "1" : "0", 1);
  }
  void putNonBlocking(const std::string &key, const std::string &val);
+  void flushNonBlockingWrites();
  inline void putBoolNonBlocking(const std::string &key, bool val) {
    putNonBlocking(key, val ? "1" : "0");
  }
@@ -73,6 +73,7 @@ params_get = _bind("params_get", [ParamsHandle, ctypes.c_char_p, ctypes.c_bool],
 params_get_bool = _bind("params_get_bool", [ParamsHandle, ctypes.c_char_p, ctypes.c_bool], ctypes.c_bool)
 params_put = _bind("params_put", [ParamsHandle, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_size_t, ctypes.c_bool], ctypes.c_int)
 params_put_bool = _bind("params_put_bool", [ParamsHandle, ctypes.c_char_p, ctypes.c_bool, ctypes.c_bool], ctypes.c_int)
+params_flush = _bind("params_flush", [ParamsHandle])
 params_remove = _bind("params_remove", [ParamsHandle, ctypes.c_char_p], ctypes.c_int)
 params_get_path = _bind("params_get_path", [ParamsHandle, ctypes.c_char_p, ctypes.c_size_t], ParamsBuffer)
 params_keys_size = _bind("params_keys_size", [ParamsHandle], ctypes.c_size_t)
@@ -178,6 +179,10 @@ class Params:
  def put_bool(self, key, val, block=False):
    params_put_bool(self.p, self.check_key(key), val, block)

+  def flush(self):
+    """Wait for all prior nonblocking writes from this Params instance."""
+    params_flush(self.p)
+
  def remove(self, key):
    params_remove(self.p, self.check_key(key))

@@ -133,6 +133,12 @@ int params_put_bool(ParamsHandle *handle, const char *key, bool value, bool bloc
  });
 }

+void params_flush(ParamsHandle *handle) noexcept {
+  translate_exceptions([&]() {
+    handle->params.flushNonBlockingWrites();
+  });
+}
+
 int params_remove(ParamsHandle *handle, const char *key) noexcept {
  return translate_exceptions(-1, [&]() {
    return handle->params.remove(key);
@@ -162,12 +168,15 @@ ParamsBuffer params_key_at(ParamsHandle *handle, size_t index) noexcept {

 size_t params_keys_by_flag(ParamsHandle *handle, uint32_t flag, ParamsBuffer *out, size_t out_size) noexcept {
  return translate_exceptions(size_t{0}, [&]() {
-    auto filtered = handle->params.allKeys(static_cast<ParamKeyFlag>(flag));
-    size_t count = std::min(filtered.size(), out_size);
-    for (size_t i = 0; i < count; i++) {
-      out[i] = return_string(filtered[i]);
+    size_t count = 0;
+    for (const auto &key : handle->keys) {
+      if (flag == ALL || (handle->params.getKeyFlag(key) & flag)) {
+        // Each buffer borrows a different string, stable for the handle's lifetime.
+        if (count < out_size) out[count] = {key.data(), key.size()};
+        ++count;
+      }
    }
-    return filtered.size();
+    return count;
  });
 }

@@ -59,7 +59,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"IsDriverViewEnabled", {CLEAR_ON_MANAGER_START, BOOL}},
    {"IsEngaged", {PERSISTENT, BOOL}},
    {"IsLdwEnabled", {PERSISTENT | BACKUP, BOOL}},
-    {"IsLiveStreaming", {CLEAR_ON_MANAGER_START, BOOL}},
+    {"IsLiveStreaming", {CLEAR_ON_MANAGER_START | CLEAR_ON_IGNITION_ON, BOOL}},
    {"IsMetric", {PERSISTENT | BACKUP, BOOL}},
    {"IsOffroad", {CLEAR_ON_MANAGER_START, BOOL}},
    {"IsRhdDetected", {PERSISTENT, BOOL}},
@@ -92,6 +92,12 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"ObdMultiplexingEnabled", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, BOOL}},
    {"Offroad_CarUnrecognized", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
    {"Offroad_ChestnutBranch", {CLEAR_ON_MANAGER_START, JSON}},
+    {"Offroad_ChestnutNotDetected", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
+    {"Offroad_ChestnutOverheated", {CLEAR_ON_MANAGER_START, JSON}},
+    {"Offroad_ChestnutPcieUnavailable", {CLEAR_ON_MANAGER_START, JSON}},
+    {"Offroad_ChestnutUncompiled", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
+    {"Offroad_ChestnutUpdateFailed", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
+    {"Offroad_ChestnutUsbSlow", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
    {"Offroad_ConnectivityNeeded", {CLEAR_ON_MANAGER_START, JSON}},
    {"Offroad_ConnectivityNeededPrompt", {CLEAR_ON_MANAGER_START, JSON}},
    {"Offroad_ExcessiveActuation", {PERSISTENT, JSON}},
@@ -130,12 +136,15 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"UpdaterLastFetchTime", {PERSISTENT, TIME}},
    {"UptimeOffroad", {PERSISTENT, FLOAT, "0.0"}},
    {"UptimeOnroad", {PERSISTENT, FLOAT, "0.0"}},
-    {"UsbGpuActive", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
-    {"UsbGpuLoading", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
+    {"ChestnutActive", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
+    {"ChestnutLoading", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
+    {"ChestnutModelError", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
    {"Version", {PERSISTENT, STRING}},

    // --- sunnypilot params --- //
    {"ApiCache_DriveStats", {PERSISTENT, JSON}},
+    {"AssistedDrivingMilestonesEnabled", {PERSISTENT | BACKUP, BOOL, "1"}},
+    {"AssistedDrivingMilestoneState", {PERSISTENT, JSON, "{}"}},
    {"AutoLaneChangeBsmDelay", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"AutoLaneChangeTimer", {PERSISTENT | BACKUP, INT, "0"}},
    {"BlinkerLateralReengageDelay", {PERSISTENT | BACKUP, INT, "0"}},  // seconds
@@ -156,6 +165,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"DevUIInfo", {PERSISTENT | BACKUP, INT, "0"}},
    {"EnableCopyparty", {PERSISTENT | BACKUP, BOOL}},
    {"EnableGithubRunner", {PERSISTENT | BACKUP, BOOL}},
+    {"FullAssistDrivenDistanceMeters", {PERSISTENT, FLOAT, "0.0"}},
    {"GreenLightAlert", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"GithubRunnerSufficientVoltage", {CLEAR_ON_MANAGER_START , BOOL}},
    {"HasAcceptedTermsSP", {PERSISTENT, STRING, "0"}},
@@ -165,7 +175,9 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"IsDevelopmentBranch", {CLEAR_ON_MANAGER_START, BOOL}},
    {"IsReleaseSpBranch", {CLEAR_ON_MANAGER_START, BOOL}},
    {"LastGPSPositionLLK", {PERSISTENT, STRING}},
+    {"LastDriveAssistedDrivingSummary", {PERSISTENT, JSON, "{}"}},
    {"LeadDepartAlert", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"MadsDrivenDistanceMeters", {PERSISTENT, FLOAT, "0.0"}},
    {"MaxTimeOffroad", {PERSISTENT | BACKUP, INT, "1800"}},
    {"ModelRunnerTypeCache", {CLEAR_ON_ONROAD_TRANSITION, INT}},
    {"OffroadMode", {CLEAR_ON_MANAGER_START, BOOL}},
@@ -195,14 +207,16 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {

    // Model Manager params
    {"ModelManager_ActiveBundle", {PERSISTENT, JSON}},
-    {"ModelManager_ActiveJson", {CLEAR_ON_MANAGER_START, STRING}},
+    {"ModelManager_ActiveBundleUSBGPU", {PERSISTENT, JSON}}, //TODO-SP: kept for migration, remove on next sync?
+    {"ModelManager_ActiveBundleChestnut", {PERSISTENT, JSON}},
+    {"ModelManager_ActiveJson", {CLEAR_ON_MANAGER_START, JSON}},
    {"ModelManager_ClearCache", {CLEAR_ON_MANAGER_START, BOOL}},
-    {"ModelManager_DownloadIndex", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, INT}},
+    {"ModelManager_DownloadRef", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, STRING}},
    {"ModelManager_Favs", {PERSISTENT | BACKUP, STRING}},
    {"ModelManager_LastSyncTime", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION, INT, "0"}},
-    {"ModelManager_LastSyncTime_USBGPU", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION, INT, "0"}},
+    {"ModelManager_LastSyncTime_Chestnut", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION, INT, "0"}},
    {"ModelManager_ModelsCache", {PERSISTENT | BACKUP, JSON}},
-    {"ModelManager_ModelsCache_USBGPU", {PERSISTENT | BACKUP, JSON}},
+    {"ModelManager_ModelsCache_Chestnut", {PERSISTENT | BACKUP, JSON}},

    // Neural Network Lateral Control
    {"NeuralNetworkLateralControl", {PERSISTENT | BACKUP, BOOL, "0"}},
@@ -223,6 +237,8 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"BackupManager_RestoreVersion", {PERSISTENT, STRING}},

    // sunnypilot car specific params
+    {"FordPscmObserver", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"FordModelActionController", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"HyundaiLongitudinalTuning", {PERSISTENT | BACKUP, INT, "0"}},
    {"SubaruStopAndGo", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"SubaruStopAndGoManualParkingBrake", {PERSISTENT | BACKUP, BOOL, "0"}},
@@ -245,6 +261,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {

    // mapd
    {"MapAdvisorySpeedLimit", {CLEAR_ON_ONROAD_TRANSITION, FLOAT}},
+    {"Mapd_ClearCache", {CLEAR_ON_MANAGER_START, BOOL}},
    {"MapdVersion", {PERSISTENT, STRING}},
    {"MapSpeedLimit", {CLEAR_ON_ONROAD_TRANSITION, FLOAT, "0.0"}},
    {"NextMapSpeedLimit", {CLEAR_ON_ONROAD_TRANSITION, JSON}},
@@ -27,14 +27,14 @@ public:
    auto param_path = Params().getParamPath();
    if (util::file_exists(param_path)) {
      std::string real_path = util::readlink(param_path);
-      util::check_system(util::string_format("rm %s -rf", real_path.c_str()));
+      util::check_system(util::string_format("rm -rf %s", real_path.c_str()));
      unlink(param_path.c_str());
    }
    if (getenv("COMMA_CACHE") == nullptr) {
-      util::check_system(util::string_format("rm %s -rf", Path::download_cache_root().c_str()));
+      util::check_system(util::string_format("rm -rf %s", Path::download_cache_root().c_str()));
    }
-    util::check_system(util::string_format("rm %s -rf", Path::comma_home().c_str()));
-    util::check_system(util::string_format("rm %s -rf", msgq_path.c_str()));
+    util::check_system(util::string_format("rm -rf %s", Path::comma_home().c_str()));
+    util::check_system(util::string_format("rm -rf %s", msgq_path.c_str()));
    unsetenv("OPENPILOT_PREFIX");
  }

@@ -106,6 +106,13 @@ class TestParams(OpenpilotTestCase):
    assert q.get("CarParams") is None
    assert q.get("CarParams", True) == b"1"

+  def test_flush_non_blocking_writes(self):
+    self.params.put("DongleId", "first")
+    self.params.put("DongleId", "last")
+    self.params.flush()
+
+    assert self.params.get("DongleId") == "last"
+
  def test_params_all_keys(self):
    keys = Params().all_keys()

@@ -126,6 +133,16 @@ class TestParams(OpenpilotTestCase):
    assert self.params.get("LiveParametersV2") is None
    assert self.params.get("LiveParametersV2", return_default=True) is None

+  def test_filtered_keys_are_distinct_registered_strings(self):
+    registered = set(self.params.all_keys())
+    for flag in (ParamKeyFlag.PERSISTENT, ParamKeyFlag.BACKUP, ParamKeyFlag.CLEAR_ON_MANAGER_START):
+      filtered = self.params.all_keys(flag)
+      assert len(filtered) > 1
+      assert len(filtered) == len(set(filtered))
+      assert set(filtered) <= registered
+      assert all(key.decode('utf-8') for key in filtered)
+      assert self.params.all_keys(flag) == filtered
+
  def test_params_get_type(self):
    # json
    self.params.put("ApiCache_FirehoseStats", {"a": 0}, block=True)
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:845c40ff0d37612e8f2f482a36845744b5ae91ce2fcfc8117990d7d278b59820
+size 13079
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8a8c5fece2a1c7587feb41cbe04c6aee08e768ecd9b5d00da6af9832a4ccc842
-size 2034
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7409c53d7c72681c24982fd83b56ce70f80797c9c0f936d9296a5c18557ac472
-size 7279
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:58bd6155433f623b1f75d134bd8ca4745d9aa71f6767eb807cdbcf7deb3089a1
-size 10876
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07bda2fe5d6be0b2854044053c384fe002e96406da119863a443b9344258b500
+size 1544
@@ -21,6 +21,7 @@ from opendbc.car.interfaces import CarInterfaceBase, RadarInterfaceBase
 from openpilot.selfdrive.pandad import can_capnp_to_list, can_list_to_can_capnp
 from openpilot.selfdrive.car.cruise import VCruiseHelper
 from openpilot.selfdrive.car.helpers import convert_carControlSP, convert_to_capnp
+from openpilot.selfdrive.car.ford_pscm_status import populate_ford_pscm_status

 from openpilot.sunnypilot.mads.helpers import set_alternative_experience, set_car_specific_params
 from openpilot.sunnypilot.selfdrive.car import interfaces as sunnypilot_interfaces
@@ -198,6 +199,7 @@ class Car:
    # Update carState from CAN
    CS, CS_SP = self.CI.update(can_list)
    CS_SP = convert_to_capnp(CS_SP)
+    populate_ford_pscm_status(self.CP, self.CI.can_parsers, CS_SP, CS.canValid)

    # Update radar tracks from CAN
    RD: structs.RadarDataT | None = self.RI.update(can_list)
@@ -0,0 +1,36 @@
+"""Publish the Ford PSCM's actual CAN status without changing opendbc structs."""
+import math
+
+from opendbc.car import Bus
+from opendbc.car.ford.values import FordFlags
+
+
+MESSAGE = 'Lane_Assist_Data3_FD1'
+SIGNALS = ('LatCtlSte_D_Stat', 'LatCtlLim_D_Stat', 'LatCtlCpblty_D_Stat', 'LaActDeny_B_Actl')
+
+
+def populate_ford_pscm_status(CP, can_parsers, CS_SP, can_valid):
+  if CP.brand != 'ford' or not CP.flags & FordFlags.CANFD:
+    return
+  status = CS_SP.init('fordPscmStatus')
+  parser = can_parsers.get(Bus.pt)
+  if parser is None:
+    return
+  values = parser.vl.get(MESSAGE, {})
+  timestamps = parser.ts_nanos.get(MESSAGE, {})
+  if any(signal not in values or signal not in timestamps for signal in SIGNALS):
+    return
+  received = timestamps[SIGNALS[0]]
+  if received <= 0 or any(timestamps[signal] != received for signal in SIGNALS):
+    return
+  decoded = [values[signal] for signal in SIGNALS]
+  if any(not math.isfinite(value) or int(value) != value or not 0 <= value <= maximum
+         for value, maximum in zip(decoded, (7, 3, 3, 1), strict=True)):
+    return
+  status.canMonoTime = received
+  status.lateralState, status.limit, status.capability = map(int, decoded[:3])
+  status.denied = bool(decoded[3])
+  # CI.update already checked all parser validity. Reading can_valid again here
+  # would advance the parser's invalid-message counter a second time per tick.
+  # Age is evaluated by the feedback consumer using this original CAN timestamp.
+  status.valid = bool(can_valid)
@@ -63,5 +63,6 @@ def convert_carControlSP(struct: capnp.lib.capnp._DynamicStructReader) -> struct
  struct_dataclass.intelligentCruiseButtonManagement = structs.IntelligentCruiseButtonManagement(
    **remove_deprecated(struct_dict.get('intelligentCruiseButtonManagement', {}))
  )
+  struct_dataclass.fordLateralPath = structs.FordLateralPath(**remove_deprecated(struct_dict.get('fordLateralPath', {})))

  return struct_dataclass
@@ -0,0 +1,109 @@
+import ast
+from pathlib import Path
+from types import SimpleNamespace
+import unittest
+
+from openpilot.cereal import custom
+from openpilot.selfdrive.car.ford_pscm_status import MESSAGE, SIGNALS, populate_ford_pscm_status
+from openpilot.selfdrive.car.helpers import convert_to_capnp
+from opendbc.can import CANPacker, CANParser
+from opendbc.car import Bus, structs
+from opendbc.car.ford.values import FordFlags
+
+
+class TestFordPscmStatus(unittest.TestCase):
+  def setUp(self):
+    self.cp = SimpleNamespace(brand='ford', flags=FordFlags.CANFD)
+    self.packer = CANPacker('ford_lincoln_base_pt')
+    self.parser = CANParser('ford_lincoln_base_pt', [(MESSAGE, 33), ('Yaw_Data_FD1', 100)], 0)
+
+  def update_status(self, timestamp, *, lateral_state=2, limit=0, capability=2, denied=False):
+    status = self.packer.make_can_msg(MESSAGE, 0, dict(zip(SIGNALS, (lateral_state, limit, capability, denied), strict=True)))
+    yaw = self.packer.make_can_msg('Yaw_Data_FD1', 0, {'VehYaw_W_Actl': 0.1})
+    self.parser.update([(timestamp, [status, yaw])])
+
+  def publish(self, *, can_valid=True):
+    state_sp = convert_to_capnp(structs.CarStateSP(speedLimit=13.5))
+    populate_ford_pscm_status(self.cp, {Bus.pt: self.parser}, state_sp, can_valid)
+    return state_sp
+
+  def test_decodes_status_and_preserves_receipt_time_across_other_can_messages(self):
+    self.update_status(1_000_000_000, limit=2, capability=1, denied=True)
+    original = self.publish()
+    self.assertEqual(original.speedLimit, 13.5)
+    status = original.fordPscmStatus
+    self.assertTrue(status.valid)
+    self.assertEqual(status.canMonoTime, 1_000_000_000)
+    self.assertEqual((status.lateralState, status.limit, status.capability, status.denied), (2, 2, 1, True))
+
+    # carStateSP may publish at 100 Hz while this 33 Hz message is absent. New
+    # unrelated CAN must not freshen the timestamp of an old PSCM status.
+    yaw = self.packer.make_can_msg('Yaw_Data_FD1', 0, {'VehYaw_W_Actl': .2})
+    self.parser.update([(1_080_000_000, [yaw])])
+    copied = self.publish().fordPscmStatus
+    self.assertEqual(copied.canMonoTime, 1_000_000_000)
+    self.assertEqual((copied.limit, copied.capability, copied.denied), (2, 1, True))
+
+    self.update_status(1_090_000_000, lateral_state=3, limit=3, capability=2)
+    next_state = self.publish()
+    with custom.CarStateSP.from_bytes(next_state.to_bytes()) as decoded:
+      latest = decoded.fordPscmStatus
+      self.assertTrue(latest.valid)
+      self.assertEqual(latest.canMonoTime, 1_090_000_000)
+      self.assertEqual((latest.lateralState, latest.limit, latest.capability, latest.denied), (3, 3, 2, False))
+
+  def test_absent_parser_unseen_message_and_invalid_can_do_not_claim_valid_status(self):
+    state = custom.CarStateSP.new_message()
+    populate_ford_pscm_status(self.cp, {}, state, True)
+    self.assertFalse(state.fordPscmStatus.valid)
+    self.assertEqual(state.fordPscmStatus.canMonoTime, 0)
+    self.assertFalse(self.publish().fordPscmStatus.valid)
+    self.update_status(1_000_000_000)
+    invalid = self.publish(can_valid=False).fordPscmStatus
+    self.assertFalse(invalid.valid)
+    self.assertEqual(invalid.canMonoTime, 1_000_000_000)
+
+  def test_mixed_timestamps_or_malformed_status_cannot_enable_feedback(self):
+    self.update_status(1_000_000_000)
+    self.parser.ts_nanos[MESSAGE][SIGNALS[-1]] = 990_000_000
+    self.assertFalse(self.publish().fordPscmStatus.valid)
+    self.parser.ts_nanos[MESSAGE][SIGNALS[-1]] = 1_000_000_000
+    for value in (float('nan'), -1, 1.5, 4):
+      self.parser.vl[MESSAGE]['LatCtlLim_D_Stat'] = value
+      self.assertFalse(self.publish().fordPscmStatus.valid)
+
+  def test_other_vehicles_and_legacy_messages_default_to_unavailable(self):
+    for cp in (SimpleNamespace(brand='toyota'), SimpleNamespace(brand='ford', flags=0)):
+      state = custom.CarStateSP.new_message(speedLimit=10.)
+      populate_ford_pscm_status(cp, {}, state, True)
+      self.assertFalse(state.fordPscmStatus.valid)
+      self.assertEqual(state.fordPscmStatus.canMonoTime, 0)
+      self.assertEqual(state.speedLimit, 10.)
+    # Old recordings/readers have no appended status pointer; defaults must
+    # remain unavailable rather than interpreting zeroed enums as fresh data.
+    self.assertFalse(custom.CarStateSP.new_message().fordPscmStatus.valid)
+
+  def test_actual_card_update_populates_status_after_dataclass_conversion(self):
+    self.update_status(1_000_000_000, limit=1)
+    source_path = Path(__file__).resolve().parents[1] / 'card.py'
+    source = ast.parse(source_path.read_text())
+    car_class = next(n for n in source.body if isinstance(n, ast.ClassDef) and n.name == 'Car')
+    method = next(n for n in car_class.body if isinstance(n, ast.FunctionDef) and n.name == 'state_update')
+    statements = method.body
+    first = next(i for i, n in enumerate(statements) if isinstance(n, ast.Assign) and ast.unparse(n.value) == 'self.CI.update(can_list)')
+    last = next(i for i, n in enumerate(statements) if isinstance(n, ast.Expr) and isinstance(n.value, ast.Call)
+                and isinstance(n.value.func, ast.Name) and n.value.func.id == 'populate_ford_pscm_status')
+    self.assertGreater(last, first)
+    code = compile(ast.Module(body=statements[first:last + 1], type_ignores=[]), str(source_path), 'exec')
+    ci = SimpleNamespace(update=lambda _: (SimpleNamespace(canValid=True), structs.CarStateSP(speedLimit=11.)),
+                         can_parsers={Bus.pt: self.parser})
+    environment = {'self': SimpleNamespace(CP=self.cp, CI=ci), 'can_list': [], 'convert_to_capnp': convert_to_capnp,
+                   'populate_ford_pscm_status': populate_ford_pscm_status}
+    exec(code, environment)
+    self.assertTrue(environment['CS_SP'].fordPscmStatus.valid)
+    self.assertEqual(environment['CS_SP'].fordPscmStatus.canMonoTime, 1_000_000_000)
+    self.assertEqual(environment['CS_SP'].fordPscmStatus.limit, 1)
+
+
+if __name__ == '__main__':
+  unittest.main()
@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 import math
+import time
 from numbers import Number

 from openpilot.cereal import log
@@ -11,8 +12,11 @@ from openpilot.common.realtime import config_realtime_process, DT_CTRL, Priority
 from openpilot.common.swaglog import cloudlog

 from opendbc.car.car_helpers import interfaces
+from opendbc.car.ford.values import FordFlags
 from opendbc.car.vehicle_model import VehicleModel
 from openpilot.selfdrive.controls.lib.drive_helpers import clip_curvature
+from openpilot.selfdrive.controls.lib.ford_model_action import FordModelActionController, select_model_action_controller
+from openpilot.selfdrive.controls.lib.ford_path import FordPath, FordPathController, FordPscmObserverPathController
 from openpilot.selfdrive.controls.lib.latcontrol import LatControl
 from openpilot.selfdrive.controls.lib.latcontrol_pid import LatControlPID
 from openpilot.selfdrive.controls.lib.latcontrol_angle import LatControlAngle, STEER_ANGLE_SATURATION_THRESHOLD
@@ -44,7 +48,7 @@ class Controls(ControlsExt):
    self.CI = interfaces[self.CP.carFingerprint](self.CP, self.CP_SP)

    self.sm = messaging.SubMaster(['lateralDelay', 'vehicleParameters', 'lateralTorqueParameters', 'modelV2', 'selfdriveState',
-                                   'extrinsicsCalibration', 'deviceMotion', 'longitudinalPlan', 'lateralManeuverPlan', 'carState', 'carOutput',
+                                   'extrinsicsCalibration', 'deviceMotion', 'longitudinalPlan', 'lateralManeuverPlan', 'carState', 'carStateSP', 'carOutput',
                                   'driverMonitoringState', 'onroadEvents', 'driverAssistance'] + self.sm_services_ext,
                                  poll='selfdriveState')
    self.pm = messaging.PubMaster(['carControl', 'controlsState'] + self.pm_services_ext)
@@ -52,6 +56,15 @@ class Controls(ControlsExt):
    self.steer_limited_by_safety = False
    self.curvature = 0.0
    self.desired_curvature = 0.0
+    self.ford_pscm_observer = (self.CP.brand == "ford" and self.CP.flags & FordFlags.CANFD and
+                               self.params.get_bool("FordPscmObserver"))
+    self.ford_path_controller = FordPscmObserverPathController() if self.ford_pscm_observer else FordPathController()
+    self.ford_path_controller = select_model_action_controller(self.CP, self.params.get_bool("FordModelActionController"),
+                                                              self.ford_path_controller)
+    self.ford_model_action = isinstance(self.ford_path_controller, FordModelActionController)
+    if self.CP.brand == "ford":
+      cloudlog.event("Ford path controller selected", controller=type(self.ford_path_controller).__name__)
+    self.ford_path = FordPath()

    self.pose_calibrator = PoseCalibrator()
    self.calibrated_pose: Pose | None = None
@@ -155,6 +168,36 @@ class Controls(ControlsExt):
      actuators.curvature = float(lateral_output)
    else:
      actuators.steeringAngleDeg = float(lateral_output)
+    if self.CP.brand == "ford":
+      ford_model = model_v2 if self.sm.valid['modelV2'] else None
+      if self.ford_model_action:
+        reference_service = 'lateralManeuverPlan' if self.sm.valid['lateralManeuverPlan'] else 'modelV2'
+        self.ford_path = self.ford_path_controller.update(
+          ford_model, self.desired_curvature, current_curvature=self.curvature, yaw_rate=-CS.yawRate, speed=CS.vEgo, now=time.monotonic(),
+          measurement_time=self.sm.logMonoTime['carState'] * 1e-9,
+          model_time=self.sm.logMonoTime['modelV2'] * 1e-9,
+          reference_time=self.sm.logMonoTime[reference_service] * 1e-9,
+          active=CC.latActive, valid=CS.canValid and self.sm.all_checks(['carState', 'vehicleParameters', 'modelV2', reference_service]),
+          driver_pressed=CS.steeringPressed, driver_torque=CS.steeringTorque,
+          pscm_status=self.sm['carStateSP'].fordPscmStatus if self.sm.valid['carStateSP'] else None,
+        )
+        if not self.ford_path.valid:
+          CC.latActive = False
+        if self.sm.frame % 20 == 0:
+          cloudlog.event("Ford C2-free path tracking", model_mono_time=self.sm.logMonoTime['modelV2'],
+                         measurement_mono_time=self.sm.logMonoTime['carState'],
+                         reference_service=reference_service, reference_mono_time=self.sm.logMonoTime[reference_service],
+                         measured_curvature=self.curvature,
+                         **self.ford_path_controller.diagnostics)
+      elif self.ford_pscm_observer:
+        self.ford_path = self.ford_path_controller.update(ford_model, self.desired_curvature,
+                                                          current_curvature=self.curvature, v_ego=CS.vEgo,
+                                                          v_ego_raw=CS.vEgoRaw, active=CC.latActive)
+      else:
+        self.ford_path = self.ford_path_controller.update(ford_model, self.desired_curvature,
+                                                          current_curvature=self.curvature, v_ego=CS.vEgo,
+                                                          active=CC.latActive)
+      actuators.curvature = float(self.ford_path.curvature)
    # Ensure no NaNs/Infs
    for p in ACTUATOR_FIELDS:
      attr = getattr(actuators, p)
@@ -0,0 +1,181 @@
+"""Experimental Ford C2-free controller with measured-curvature C1 feedback.
+
+Selected only by its explicit toggle. The 7 m station and one-second scale are
+engineering choices. Feeding integrated heading mismatch into C1 at 1:1 is an
+explicit feedback-strength choice, not an identified PSCM model or calibration.
+"""
+import math
+import struct
+
+import numpy as np
+
+from opendbc.car.ford.values import CarControllerParams, FordFlags
+from openpilot.selfdrive.controls.lib.ford_path import FordPath, _model_path
+
+
+OFFSET_STATION_M = 7.0
+HEADING_TIME_S = 1.0
+CALIBRATION_APPROVED = False
+
+
+def _packed(value, resolution, offset):
+  """Mirror Float32 carControlSP and sign-reversed CANPacker rounding."""
+  value = struct.unpack("f", struct.pack("f", value))[0]
+  return -(math.floor((-value - offset) / resolution + 0.5) * resolution + offset)
+
+
+def _finite(*values):
+  try:
+    return all(math.isfinite(value) for value in values)
+  except (TypeError, ValueError, OverflowError):
+    return False
+
+
+def encode_model_action(model, desired_curvature, speed):
+  """Encode y(7) and max(7, v*1s)*selected limited curvature.
+
+  Preserve the reviewed core's endpoint hold when the path ends before 7 m.
+  This samples the available geometry; it does not extrapolate an unseen path.
+  """
+  if not _finite(desired_curvature, speed) or not .3 <= speed <= 55 or abs(desired_curvature) > 1:
+    return FordPath()
+  try:
+    path = _model_path(model)
+  except OverflowError:
+    return FordPath()
+  if path is None or not all(_finite(*values) for values in path):
+    return FordPath()
+  station, _, lateral, _ = path
+  c0 = float(np.interp(min(OFFSET_STATION_M, station[-1]), station, lateral))
+  c1 = max(OFFSET_STATION_M, speed*HEADING_TIME_S)*desired_curvature
+  return FordPath(True, c0, c1, 0., 0.) if _finite(c0, c1) else FordPath()
+
+
+class ModelActionController:
+  """Unquantized C0/C1 slew positions and one C1 feedback correction.
+
+  Feedback integrates requested minus measured curvature over traveled distance.
+  Freshness, measurement cadence and driver/PSCM arbitration belong to the caller.
+  """
+  __slots__ = ('c0', 'c1', 'correction')
+
+  def __init__(self):
+    self.reset()
+
+  def reset(self):
+    self.c0 = self.c1 = self.correction = 0.
+
+  def update(self, model, desired_curvature, *, current_curvature, speed, dt, active=True, valid=True,
+             feedback_dt=None, feedback_enabled=True, pscm_limited=False):
+    feedback_dt = dt if feedback_dt is None else feedback_dt
+    if (not active or not valid or not _finite(dt, feedback_dt, current_curvature) or not .002 <= dt <= .1
+        or not 0. <= feedback_dt <= .15 or abs(current_curvature) > 1.):
+      self.reset()
+      return FordPath()
+    target = encode_model_action(model, desired_curvature, speed)
+    if not target.valid:
+      self.reset()
+      return FordPath()
+    c0 = float(np.clip(target.path_offset, -5.11, 5.11))
+    base_c1 = float(np.clip(target.path_angle, -.5, .5))
+    lower = max(-.5, self.c1-.5*dt)
+    upper = min(.5, self.c1+.5*dt)
+    if not feedback_enabled:
+      self.correction = 0.
+    else:
+      increment = (desired_curvature-current_curvature)*speed*feedback_dt
+      # LimitReached inhibits only extra demand in the measured turn direction.
+      # Opposing correction and changes to the model request remain available.
+      direction = current_curvature if current_curvature else self.c1
+      if pscm_limited and increment*direction > 0.:
+        # An old opposing correction may return to zero; don't trap it below
+        # the base request just because the PSCM now reports a limit.
+        increment = float(np.clip(increment, min(-self.correction, 0.), max(-self.correction, 0.)))
+      # Integrate only as far as this cycle's amplitude/slew envelope permits.
+      # If the base moved outside that envelope, allow increments toward it;
+      # never rewrite existing correction merely because the base changed.
+      request = base_c1+self.correction
+      self.correction += float(np.clip(increment, min(lower-request, 0.), max(upper-request, 0.)))
+    self.c0 += float(np.clip(c0-self.c0, -4.*dt, 4.*dt))
+    c1 = float(np.clip(base_c1+self.correction, -.5, .5))
+    self.c1 += float(np.clip(c1-self.c1, -.5*dt, .5*dt))
+    return FordPath(True, _packed(self.c0, .01, -5.12), _packed(self.c1, .0005, -.5), 0., 0.)
+
+
+class FordModelActionController:
+  """Input adapter for the opt-in selected-action controller.
+
+  controlsd owns upstream selection/limiting and service health. This adapter
+  checks ages and clock order, then supplies elapsed time to the three-state
+  core. Feedback advances once per fresh steering measurement; repeated samples
+  can still advance output slew. Raw model geometry is checked on every cycle.
+
+  CAN yaw remains a health gate, not the feedback measurement. Driver override
+  clears the correction. Fresh PSCM limits only inhibit outward integration;
+  neither a limit nor a repeated measurement freezes the model request.
+  """
+  def __init__(self):
+    self.core = ModelActionController()
+    self.reset()
+
+  def reset(self, status='inactive'):
+    self.core.reset()
+    self.last_time = self.last_measurement_time = self.last_model_time = None
+    self.diagnostics = {'status': status, 'hypothesis': 'model-action-c1-feedback-v1',
+                        'calibration_approved': CALIBRATION_APPROVED, 'command': (0., 0., 0., 0.)}
+
+  def update(self, model, desired_curvature, *, current_curvature, yaw_rate, speed, now, measurement_time, model_time,
+             reference_time, active, valid=True, driver_pressed=False, driver_torque=0., pscm_status=None):
+    reason = None
+    if not active:
+      reason = 'inactive'
+    elif not valid:
+      reason = 'invalid_service'
+    elif not _finite(desired_curvature, current_curvature, yaw_rate, speed, now, measurement_time, model_time, reference_time):
+      reason = 'nonfinite'
+    elif not all(-.005 <= now - timestamp <= .15 for timestamp in (measurement_time, model_time, reference_time)):
+      reason = 'stale_input'
+    elif not .3 <= speed <= 55 or abs(yaw_rate) > 3 or abs(desired_curvature) > 1 or abs(current_curvature) > 1:
+      reason = 'input_range'
+    if reason is not None:
+      self.reset(reason)
+      return FordPath()
+
+    dt = .01 if self.last_time is None else now - self.last_time
+    feedback_dt = 0. if self.last_measurement_time is None else measurement_time-self.last_measurement_time
+    if not .002 <= dt <= .1 or not 0. <= feedback_dt <= .15 or (
+      self.last_model_time is not None and model_time < self.last_model_time
+    ):
+      self.reset('timing_reset')
+      return FordPath()
+    status_fresh = (pscm_status is not None and pscm_status.valid and pscm_status.canMonoTime > 0
+                    and -.005 <= now-pscm_status.canMonoTime*1e-9 <= .15)
+    pscm_limited = bool(status_fresh and pscm_status.limit == 2)
+    driver_override = bool(driver_pressed or not _finite(driver_torque)
+                           or abs(driver_torque) > CarControllerParams.STEER_DRIVER_ALLOWANCE
+                           or (status_fresh and pscm_status.limit == 3))
+    feedback_enabled = not (driver_override or (status_fresh and (pscm_status.denied or pscm_status.lateralState != 2)))
+    command = self.core.update(model, desired_curvature, current_curvature=current_curvature, speed=speed, dt=dt,
+                               feedback_dt=feedback_dt, feedback_enabled=feedback_enabled, pscm_limited=pscm_limited)
+    if not command.valid:
+      self.reset('invalid_path')
+      return command
+    self.last_time, self.last_measurement_time, self.last_model_time = now, measurement_time, model_time
+    self.diagnostics = {'status': 'active', 'hypothesis': 'model-action-c1-feedback-v1',
+                        'calibration_approved': CALIBRATION_APPROVED, 'desired_curvature': desired_curvature,
+                        'model_age': now - model_time, 'measurement_age': now - measurement_time, 'reference_age': now - reference_time,
+                        'dt': dt, 'offset_request': self.core.c0, 'heading_request': self.core.c1,
+                        'curvature_error': desired_curvature-current_curvature, 'feedback_dt': feedback_dt,
+                        'heading_feedforward': float(np.clip(max(OFFSET_STATION_M, speed*HEADING_TIME_S)*desired_curvature, -.5, .5)),
+                        'heading_correction': self.core.correction, 'feedback_enabled': feedback_enabled,
+                        'driver_override': driver_override, 'pscm_limited': pscm_limited, 'pscm_status_fresh': bool(status_fresh),
+                        'command': (command.path_offset, command.path_angle, 0., 0.)}
+    return command
+
+
+def select_model_action_controller(CP, enabled, previous_controller):
+  """The separate default-off toggle takes priority on the CAN FD Lightning."""
+  compatible = CP.brand == 'ford' and CP.flags & FordFlags.CANFD and CP.carFingerprint == 'FORD_F_150_LIGHTNING_MK1'
+  if enabled and compatible:
+    return FordModelActionController()
+  return previous_controller
@@ -0,0 +1,368 @@
+from collections import deque
+from dataclasses import dataclass
+import math
+
+import numpy as np
+
+from opendbc.car.ford.values import CarControllerParams
+
+
+DBC_OFFSET = (-5.12, 5.11)
+DBC_ANGLE = (-0.5, 0.5235)
+DBC_CURVATURE = (-0.02, 0.02)
+DBC_CURVATURE_RATE = (-0.001024, 0.001023)
+
+DBC_OFFSET_RESOLUTION = 0.01
+DBC_ANGLE_RESOLUTION = 0.0005
+DBC_CURVATURE_RESOLUTION = 0.00002
+DBC_CURVATURE_RATE_RESOLUTION = 0.000001
+_PATH_MIN_LOOKAHEAD = 7.0
+_POSE_PREDICTION_TIME = 0.1
+_POSE_BLEND_CURVATURE = (0.006, 0.012)
+_PATH_OFFSET_RATE = 4.0
+_PATH_ANGLE_RATE = 1.0
+
+_PSCM_DT = 0.004
+_PSCM_C0_RATE = 1.5
+_PSCM_C1_RATE = 0.100006103515625
+_PSCM_C2_RATE = 0.0030059814453125
+_PSCM_SPEED_KPH = (0.0, 15.0, 40.0, 70.0, 100.0, 150.0, 200.0, 250.0)
+_PSCM_SPEED_GAIN = (32.0, 32.0, 32.0, 30.0, 30.0, 24.0, 12.0, 0.0)
+_PSCM_C0_EFFECTIVE_LIMIT = 1.0
+_PSCM_C1_EFFECTIVE_LIMIT = 0.349609375 / 10.0
+
+
+@dataclass(frozen=True)
+class FordPath:
+  valid: bool = False
+  path_offset: float = 0.0
+  path_angle: float = 0.0
+  curvature: float = 0.0
+  curvature_rate: float = 0.0
+
+
+@dataclass(frozen=True)
+class FordPscmState:
+  path_offset: float = 0.0
+  path_angle: float = 0.0
+  curvature: float = 0.0
+
+
+@dataclass(frozen=True)
+class FordModelPose:
+  path_offset: float
+  path_angle: float
+  offset_horizon: float
+  curvature_demand: float
+  forward_angle: float
+
+
+def _finite(value: float) -> float:
+  return float(value) if math.isfinite(value) else 0.0
+
+
+def _sample(distance: float, distances: list[float], values: list[float]) -> float:
+  return float(np.interp(distance, distances, values))
+
+
+def _blend_share(demand: float) -> float:
+  lower, upper = _POSE_BLEND_CURVATURE
+  return float(np.clip((demand - lower) / (upper - lower), 0.0, 1.0))
+
+
+def _model_path(model) -> tuple[list[float], list[float], list[float], list[float]] | None:
+  try:
+    x = [float(value) for value in model.position.x]
+    y = [float(value) for value in model.position.y]
+    heading = [float(value) for value in model.orientation.z]
+  except (AttributeError, TypeError, ValueError):
+    return None
+  if len(x) < 2 or len(x) != len(y) or len(x) != len(heading):
+    return None
+  if not all(math.isfinite(value) for values in (x, y, heading) for value in values):
+    return None
+
+  distance = [0.0]
+  for i in range(1, len(x)):
+    distance.append(distance[-1] + math.hypot(x[i] - x[i - 1], y[i] - y[i - 1]))
+  if distance[-1] <= 0.0:
+    return None
+
+  unwrapped_heading = [heading[0]]
+  for value in heading[1:]:
+    delta = (value - unwrapped_heading[-1] + math.pi) % (2.0 * math.pi) - math.pi
+    unwrapped_heading.append(unwrapped_heading[-1] + delta)
+  return distance, x, y, unwrapped_heading
+
+
+def _predicted_pose(distance: float, current_curvature: float,
+                    curvature_delta: float) -> tuple[float, float, float]:
+  curvature = current_curvature + 0.5 * curvature_delta
+  heading = curvature * distance
+  if abs(curvature) < 1e-9:
+    return distance, 0.0, 0.0
+  return math.sin(heading) / curvature, (1.0 - math.cos(heading)) / curvature, heading
+
+
+def _relative_pose(target_distance: float, path: tuple[list[float], list[float], list[float], list[float]],
+                   vehicle_pose: tuple[float, float, float]) -> tuple[float, float]:
+  distance, x, y, heading = path
+  vehicle_x, vehicle_y, vehicle_heading = vehicle_pose
+  dx = _sample(target_distance, distance, x) - vehicle_x
+  dy = _sample(target_distance, distance, y) - vehicle_y
+  cosine = math.cos(vehicle_heading)
+  sine = math.sin(vehicle_heading)
+  offset = -sine * dx + cosine * dy
+  angle = math.atan2(math.sin(_sample(target_distance, distance, heading) - vehicle_heading),
+                     math.cos(_sample(target_distance, distance, heading) - vehicle_heading))
+  return offset, angle
+
+
+def _path_pose(target_distance: float,
+               path: tuple[list[float], list[float], list[float], list[float]]) -> tuple[float, float, float]:
+  distance, x, y, heading = path
+  return (_sample(target_distance, distance, x), _sample(target_distance, distance, y),
+          _sample(target_distance, distance, heading))
+
+
+def _bounded_feedback(feedforward: float, feedback: float, resolution: float, zero_path_limit: float) -> float:
+  quantization_threshold = 0.5 * resolution
+  limit = max(abs(feedforward) - resolution, 0.0) if abs(feedforward) >= quantization_threshold else zero_path_limit
+  return float(np.clip(feedback, -limit, limit))
+
+
+def _model_pose(path: tuple[list[float], list[float], list[float], list[float]],
+                current_curvature: float, curvature_delta: float, v_ego: float) -> FordModelPose:
+  distance, _, _, _ = path
+  advance = min(v_ego * _POSE_PREDICTION_TIME, distance[-1])
+  offset_horizon = min(_PATH_MIN_LOOKAHEAD, distance[-1] - advance)
+  angle_horizon = min(max(v_ego, _PATH_MIN_LOOKAHEAD), distance[-1] - advance)
+
+  # Keep the model's remaining path as feedforward. Measured vehicle motion is
+  # a separate, short delay-aligned correction, so catching the requested
+  # curvature cannot erase a turn that is still present in the model path.
+  model_pose = _path_pose(advance, path)
+  model_offset, _ = _relative_pose(advance + offset_horizon, path, model_pose)
+  _, model_angle = _relative_pose(advance + angle_horizon, path, model_pose)
+  vehicle_pose = _predicted_pose(advance, current_curvature, curvature_delta)
+  feedback_offset, feedback_angle = _relative_pose(advance, path, vehicle_pose)
+  gentle_curvature = _POSE_BLEND_CURVATURE[0]
+  feedback_offset = _bounded_feedback(model_offset, feedback_offset, DBC_OFFSET_RESOLUTION,
+                                       0.5 * gentle_curvature * advance ** 2)
+  feedback_angle = _bounded_feedback(model_angle, feedback_angle, DBC_ANGLE_RESOLUTION,
+                                      gentle_curvature * advance)
+
+  offset_curvature = 2.0 * model_offset / max(offset_horizon, 1e-3) ** 2
+  angle_curvature = model_angle / max(angle_horizon, 1e-3)
+  return FordModelPose(model_offset + feedback_offset, model_angle + feedback_angle, offset_horizon,
+                       max(abs(offset_curvature), abs(angle_curvature)), model_angle)
+
+
+def _encode_pose(pose: FordModelPose, pose_share: float, curvature: float) -> FordPath:
+  path_offset = pose_share * pose.path_offset
+  path_angle = pose_share * pose.path_angle
+  if abs(path_offset) < 0.5 * DBC_OFFSET_RESOLUTION:
+    path_offset = 0.0
+  if abs(path_angle) < 0.5 * DBC_ANGLE_RESOLUTION:
+    path_angle = 0.0
+  limited_path_angle = float(np.clip(path_angle, *DBC_ANGLE))
+  path_offset += (path_angle - limited_path_angle) * pose.offset_horizon
+  return FordPath(
+    valid=True,
+    path_offset=float(np.clip(path_offset, *DBC_OFFSET)),
+    path_angle=limited_path_angle,
+    curvature=float(np.clip(curvature, *DBC_CURVATURE)),
+    curvature_rate=0.0,
+  )
+
+
+def _encode_path(path: tuple[list[float], list[float], list[float], list[float]], desired_curvature: float,
+                 current_curvature: float, curvature_delta: float, v_ego: float) -> FordPath:
+  pose = _model_pose(path, current_curvature, curvature_delta, v_ego)
+  pose_share = _blend_share(max(pose.curvature_demand, abs(desired_curvature)))
+
+  # Match upstream's C2-only normal driving, then continuously transfer the
+  # command to the model pose for larger maneuvers. An opposing/finished model
+  # path must unload sticky C2 and retain the fast pose needed to unwind it.
+  c2_opposes_path = desired_curvature != 0.0 and desired_curvature * pose.forward_angle <= 0.0
+  if c2_opposes_path:
+    pose_share = 1.0
+    curvature = 0.0
+  else:
+    curvature = desired_curvature * (1.0 - pose_share)
+
+  return _encode_pose(pose, pose_share, curvature)
+
+
+class FordPathController:
+  """Blend normal C2 following into the model's forward C0/C1 pose."""
+
+  def __init__(self, dt: float = 0.01):
+    self.dt = dt
+    self._last_path = FordPath(valid=True)
+    self._curvature_history = deque(maxlen=max(round(_POSE_PREDICTION_TIME / dt) + 1, 2))
+
+  def _limit(self, target: FordPath) -> FordPath:
+    offset_delta = target.path_offset - self._last_path.path_offset
+    angle_delta = target.path_angle - self._last_path.path_angle
+    scale = min(
+      1.0,
+      _PATH_OFFSET_RATE * self.dt / abs(offset_delta) if offset_delta else 1.0,
+      _PATH_ANGLE_RATE * self.dt / abs(angle_delta) if angle_delta else 1.0,
+    )
+    self._last_path = FordPath(
+      True,
+      self._last_path.path_offset + scale * offset_delta,
+      self._last_path.path_angle + scale * angle_delta,
+      self._last_path.curvature + scale * (target.curvature - self._last_path.curvature),
+      0.0,
+    )
+    return self._last_path
+
+  def update(self, model, desired_curvature: float, *, current_curvature: float = 0.0,
+             v_ego: float = 0.0, active: bool = True) -> FordPath:
+    if not active:
+      self._last_path = FordPath(valid=True)
+      self._curvature_history.clear()
+      return FordPath()
+    current_curvature = _finite(current_curvature)
+    self._curvature_history.append(current_curvature)
+    curvature_delta = (current_curvature - self._curvature_history[0]
+                       if len(self._curvature_history) == self._curvature_history.maxlen else 0.0)
+    path = _model_path(model) if model is not None else None
+    if path is None:
+      return self._limit(FordPath(valid=True))
+    return self._limit(_encode_path(path, _finite(desired_curvature), current_curvature, curvature_delta,
+                                    max(_finite(v_ego), 0.0)))
+
+
+def _pscm_slew(value: float, target: float, rate: float, ticks: int) -> float:
+  step = rate * _PSCM_DT * ticks
+  return float(np.clip(target, value - step, value + step))
+
+
+def _pscm_speed_gain(v_ego: float) -> float:
+  return float(np.interp(max(v_ego, 0.0) * 3.6, _PSCM_SPEED_KPH, _PSCM_SPEED_GAIN))
+
+
+def _wire_path(path: FordPath) -> FordPath:
+  return FordPath(
+    valid=path.valid,
+    path_offset=round(path.path_offset / DBC_OFFSET_RESOLUTION) * DBC_OFFSET_RESOLUTION,
+    path_angle=round(path.path_angle / DBC_ANGLE_RESOLUTION) * DBC_ANGLE_RESOLUTION,
+    curvature=round(path.curvature / DBC_CURVATURE_RESOLUTION) * DBC_CURVATURE_RESOLUTION,
+    curvature_rate=round(path.curvature_rate / DBC_CURVATURE_RATE_RESOLUTION) * DBC_CURVATURE_RATE_RESOLUTION,
+  )
+
+
+def _pscm_contributions(state: FordPscmState, v_ego: float) -> tuple[float, float, float]:
+  gain = _pscm_speed_gain(v_ego)
+  return (
+    float(np.clip(0.5 * gain * state.path_offset, -0.5 * gain, 0.5 * gain)),
+    float(np.clip(10.0 * gain * state.path_angle, -0.349609375 * gain, 0.349609375 * gain)),
+    float(np.clip(0.30078125 * gain * state.curvature * v_ego ** 2, -0.5 * gain, 0.5 * gain)),
+  )
+
+
+class FordPscmObserver:
+  """Mirror the firmware's held-command coefficient states at its 250 Hz step."""
+
+  def __init__(self):
+    self.state = FordPscmState()
+    self.command = FordPath(valid=True)
+    self._phase = 0.0
+
+  def reset(self) -> None:
+    self.state = FordPscmState()
+    self.command = FordPath(valid=True)
+    self._phase = 0.0
+
+  def advance(self, elapsed: float) -> None:
+    self._phase += max(elapsed, 0.0)
+    ticks = int((self._phase + 1e-12) / _PSCM_DT)
+    self._phase -= ticks * _PSCM_DT
+    if ticks == 0:
+      return
+    self.state = FordPscmState(
+      _pscm_slew(self.state.path_offset, self.command.path_offset, _PSCM_C0_RATE, ticks),
+      _pscm_slew(self.state.path_angle, self.command.path_angle, _PSCM_C1_RATE, ticks),
+      _pscm_slew(self.state.curvature, self.command.curvature + 10.0 * self.command.curvature_rate,
+                  _PSCM_C2_RATE, ticks),
+    )
+
+  def set_command(self, command: FordPath) -> None:
+    self.command = _wire_path(command)
+
+
+class FordPscmObserverPathController:
+  """Compensate model-path commands for the PSCM coefficient state it still carries."""
+
+  def __init__(self, dt: float = 0.01):
+    self.dt = dt
+    self._last_path = FordPath(valid=True)
+    self._curvature_history = deque(maxlen=max(round(_POSE_PREDICTION_TIME / dt) + 1, 2))
+    self.observer = FordPscmObserver()
+    self._sent_c2 = 0.0
+
+  def _reset(self) -> None:
+    self._last_path = FordPath(valid=True)
+    self._curvature_history.clear()
+    self.observer.reset()
+    self._sent_c2 = 0.0
+
+  def _command_for_state(self, target: FordPath, v_ego: float) -> FordPath:
+    # The target describes the desired fully-settled PSCM contribution. C0 keeps
+    # the remaining C1-saturated residual. C1 supplies the primary contribution
+    # that the known slow C2 state does not yet provide, without a guessed gain.
+    target_state = FordPscmState(target.path_offset, target.path_angle, target.curvature)
+    target_contribution = sum(_pscm_contributions(target_state, v_ego))
+    _, _, observed_c2 = _pscm_contributions(self.observer.state, v_ego)
+    gain = _pscm_speed_gain(v_ego)
+    required_fast = target_contribution - observed_c2
+    c1_contribution = float(np.clip(required_fast, -0.349609375 * gain, 0.349609375 * gain))
+    c0_contribution = required_fast - c1_contribution
+    path_offset = c0_contribution / (0.5 * gain) if gain > 0.0 else 0.0
+    path_angle = c1_contribution / (10.0 * gain) if gain > 0.0 else 0.0
+    return FordPath(
+      valid=True,
+      path_offset=float(np.clip(path_offset, -_PSCM_C0_EFFECTIVE_LIMIT, _PSCM_C0_EFFECTIVE_LIMIT)),
+      path_angle=float(np.clip(path_angle, -_PSCM_C1_EFFECTIVE_LIMIT, _PSCM_C1_EFFECTIVE_LIMIT)),
+      curvature=target.curvature,
+      curvature_rate=target.curvature_rate,
+    )
+
+  def _limit(self, target: FordPath, v_ego_raw: float) -> FordPath:
+    path_offset = float(np.clip(target.path_offset,
+                                self._last_path.path_offset - _PATH_OFFSET_RATE * self.dt,
+                                self._last_path.path_offset + _PATH_OFFSET_RATE * self.dt))
+    path_angle = float(np.clip(target.path_angle,
+                               self._last_path.path_angle - _PATH_ANGLE_RATE * self.dt,
+                               self._last_path.path_angle + _PATH_ANGLE_RATE * self.dt))
+    curvature = CarControllerParams.CURVATURE_LIMITS.apply_limits(
+      target.curvature, self._sent_c2, v_ego_raw, 0.0, True, CarControllerParams.LMC2_STEP,
+    )
+    self._sent_c2 = curvature
+    self._last_path = FordPath(True, path_offset, path_angle, curvature, target.curvature_rate)
+    self.observer.set_command(self._last_path)
+    return self._last_path
+
+  def update(self, model, desired_curvature: float, *, current_curvature: float = 0.0,
+             v_ego: float = 0.0, v_ego_raw: float = 0.0, active: bool = True) -> FordPath:
+    if not active:
+      self._reset()
+      return FordPath()
+
+    self.observer.advance(self.dt)
+    current_curvature = _finite(current_curvature)
+    self._curvature_history.append(current_curvature)
+    curvature_delta = (current_curvature - self._curvature_history[0]
+                       if len(self._curvature_history) == self._curvature_history.maxlen else 0.0)
+    path = _model_path(model) if model is not None else None
+    if path is None:
+      target = FordPath(valid=True)
+    else:
+      target = _encode_path(path, _finite(desired_curvature), current_curvature, curvature_delta,
+                            max(_finite(v_ego), 0.0))
+    v_ego_raw = max(_finite(v_ego_raw), 0.0)
+    command = self._command_for_state(target, v_ego_raw)
+    return self._limit(command, v_ego_raw)
@@ -0,0 +1,229 @@
+{
+  "description": "Curvature-driven C0 and full-heading C1 command regression; does not predict counterfactual wheel response. Contains geometry and control signals only, no GPS.",
+  "fixture_sha256": "12782ac1b0d0637945f729a46ad03af16cd58188872b6a65f104e32c4db70e9b",
+  "episodes": [
+    {
+      "name": "left_large",
+      "route": "84865544361f55cb_00000077--4b55791ce6",
+      "range_seconds": [
+        809.5,
+        815.0
+      ],
+      "evidence_seconds": [
+        812.1,
+        814.0
+      ],
+      "samples": 532
+    },
+    {
+      "name": "right_large",
+      "route": "84865544361f55cb_00000077--4b55791ce6",
+      "range_seconds": [
+        866.5,
+        872.0
+      ],
+      "evidence_seconds": [
+        869.5,
+        871.0
+      ],
+      "samples": 547
+    },
+    {
+      "name": "left_very_large",
+      "route": "84865544361f55cb_00000077--4b55791ce6",
+      "range_seconds": [
+        880.0,
+        884.0
+      ],
+      "evidence_seconds": [
+        882.9,
+        883.32
+      ],
+      "samples": 397
+    },
+    {
+      "name": "right_plateau",
+      "route": "84865544361f55cb_00000077--4b55791ce6",
+      "range_seconds": [
+        964.0,
+        970.0
+      ],
+      "evidence_seconds": [
+        967.2,
+        968.93
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+      "samples": 583
+    },
+    {
+      "name": "oscillation",
+      "route": "84865544361f55cb_00000078--349f5b8695",
+      "range_seconds": [
+        29.0,
+        37.0
+      ],
+      "evidence_seconds": [
+        32.5,
+        36.1
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+      "samples": 795
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+    {
+      "name": "weak_first",
+      "route": "84865544361f55cb_0000007a--5a95fc717e",
+      "range_seconds": [
+        90.5,
+        95.2
+      ],
+      "evidence_seconds": [
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+      "samples": 467
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+    {
+      "name": "weak_second",
+      "route": "84865544361f55cb_0000007a--5a95fc717e",
+      "range_seconds": [
+        119.5,
+        125.3
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+      "samples": 576
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+  ],
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+    ]
+  },
+  "pairing": "controlsState cycle time; causal carState speed/yaw/pressed; exact consumed model timestamp and geometry; nearest same-cycle carControl and carControlSP within 5ms.",
+  "yaw_rate": "Negative carState.yawRate, matching the model/control curvature coordinate sign; no wheel-to-curvature conversion.",
+  "desired_curvature": "Exact controlsState.desiredCurvature from the matching controlsState cycle. This is the post-selection, post-limiting request consumed by controlsd; it is not a wheel-angle-to-curvature fit.",
+  "reference_time": "Exact consumed modelV2 publication time, in the same relative seconds as each episode. The extraction cache does not retain consumed lateralManeuverPlan timestamps or validity; model time is an explicit replay assumption and cannot verify alternate-reference freshness."
+}
@@ -0,0 +1,67 @@
+{
+  "description": "Real route80 turn-command regressions. Signal-only fixture; no GPS. Counterfactual commands do not predict physical vehicle response.",
+  "route": "84865544361f55cb_00000080--1643deea7e",
+  "source_commit": "98662df401217a00ec9fc8e73b16857b6c220150",
+  "frozen_v3_controller_sha256": "576f4ec6f2dbc93f7e6c93a69839f69447eb5a0c2f834bd48b24f84a163dc2eb",
+  "fixture_sha256": "c1460e2cf1d3fd52b1a036d923fec7835a7d361126ee0c2decbc3f101ee6653c",
+  "episodes": [
+    {
+      "name": "under_333_339",
+      "range_seconds": [
+        331.5,
+        339.0
+      ],
+      "evidence_seconds": [
+        333.0,
+        339.0
+      ],
+      "samples": 745
+    },
+    {
+      "name": "over_417_420",
+      "range_seconds": [
+        415.5,
+        420.0
+      ],
+      "evidence_seconds": [
+        417.0,
+        420.0
+      ],
+      "samples": 447
+    },
+    {
+      "name": "under_430_435",
+      "range_seconds": [
+        428.5,
+        435.0
+      ],
+      "evidence_seconds": [
+        430.0,
+        435.0
+      ],
+      "samples": 646
+    }
+  ],
+  "sources": [
+    {
+      "name": "84865544361f55cb_00000080--1643deea7e--5--rlog.zst",
+      "bytes": 12531711,
+      "sha256": "059482830794cb0eabe6069b75a9610b900bf2a93d7a6624f53c575cef997157"
+    },
+    {
+      "name": "84865544361f55cb_00000080--1643deea7e--6--rlog.zst",
+      "bytes": 12560505,
+      "sha256": "147276789f5b14913adc4cd16db18f3d4bd27ce8497c9ff96fdf0315c219339f"
+    },
+    {
+      "name": "84865544361f55cb_00000080--1643deea7e--7--rlog.zst",
+      "bytes": 12660797,
+      "sha256": "b311b6ace75819db52b9618154d68c7d12e2751d5046b6d174adb89ef87a223c"
+    }
+  ],
+  "pairing": "Exact controlsState desiredCurvature and consumed model publication timestamp; causal carState speed, negative CAN yaw, and steeringPressed; nearest same-cycle carControl/carControlSP within 5 ms.",
+  "reference_time": "Consumed modelV2 publication time. Controller audit confirms route80 used modelV2 as reference throughout.",
+  "preroll": "Each episode starts from reset 1.5 s before evidence; v3_replay stores those exact cold-start commands and gates, while recorded stores original live path fields.",
+  "benchmark_clean": "Existing route80 benchmark mask: whole interval request minus 0.5 s through response (0.2 s) plus 0.25 s active, unpressed, valid, fresh, and speed >= 2 m/s.",
+  "expected_common_c1": "Independent shadow: clip(desiredCurvature * max(7 m, vEgo * 1 s), +/-0.5 rad), independently slewed at 0.5 rad/s and packed to Float32/sign-reversed CAN semantics. No subtraction of measured curvature."
+}
@@ -0,0 +1,13 @@
+{
+  "description": "PSCM status and raw driver-torque overlay for the existing three route80 request windows. No GPS. No counterfactual vehicle response.",
+  "fixture_sha256": "a9defdc5abdf26724358d606beb16becbdf30faa972974d49b179a9e004d7629",
+  "base_fixture": "ford_curvature_heading_route80.npz",
+  "base_fixture_sha256": "c1460e2cf1d3fd52b1a036d923fec7835a7d361126ee0c2decbc3f101ee6653c",
+  "source_route": "84865544361f55cb_00000080--1643deea7e",
+  "source_commit": "98662df401217a00ec9fc8e73b16857b6c220150",
+  "samples": 1838,
+  "source_cache_sha256": "1cd3e0c00805869ace1c5954dc682644f36f5eddb71785b69e4cb9da40f7f04f",
+  "pairing": "Latest actual bus-0 EPS 972 frame at or before each controlsState cycle; raw steering torque from the exact causal carState used by the base fixture.",
+  "timestamp_policy": "Actual CAN event logMonoTime in route-relative seconds, not the benchmark response-shifted status. The old route predates the new carStateSP status telemetry; source CAN timestamps are an explicit replay approximation.",
+  "validity": "Replay validity uses the paired carState valid and canValid values; enum validity, availability and age are checked by the production feedback controller."
+}
@@ -0,0 +1,40 @@
+{
+  "description": "Signal-only v6 turn-exit recovery regression; no location, device identity, or predicted new vehicle response.",
+  "recorded_controller_revision": "61dac4977bf9c36504398e8a4959dfed79cf6f05",
+  "baseline_revision": "61dac4977bf9c36504398e8a4959dfed79cf6f05",
+  "response_delay": 0.20000000298023224,
+  "samples": 9134,
+  "models": 1843,
+  "fixture_sha256": "41d5e3efcee03a9e02fcaf7bf456c050c6a671a5b7f7fc9706ddf4d27bad71b8",
+  "windows": [
+    {
+      "name": "overturn_then_underturn",
+      "range_s": [
+        19.99615067150053,
+        29.48070058550053
+      ],
+      "samples": 521
+    },
+    {
+      "name": "well_tracked_curve_a",
+      "range_s": [
+        56.19474309950053,
+        63.68808603150053
+      ],
+      "samples": 729
+    },
+    {
+      "name": "well_tracked_curve_b",
+      "range_s": [
+        101.19780691350051,
+        116.33885445450052
+      ],
+      "samples": 810
+    }
+  ],
+  "selection": "One previously identified overturn-then-underturn event and two previously reported well-tracked curves; selected before recovery implementation.",
+  "mask": "Whole t-0.5 through t+0.65 interval active, valid, fresh, unpressed, raw driver torque magnitude <=1 Nm; requested |curvature|*speed\u00b2 >=.5 m/s\u00b2.",
+  "timing": "Exact consumed model publication; causal CAN/PSCM at estimated control computation time. Subtract observed median computation-to-publication delay; unsampled tick timing remains approximate.",
+  "context": "At least 20 seconds prior context or the available start, extended before the latest observed reset. Overlapping episodes are merged.",
+  "coordinates": "Times are local elapsed seconds; models contain only relative position.x/y and orientation.z arrays."
+}
@@ -0,0 +1,247 @@
+{
+  "description": "Signal-only historical fallback evidence and frozen-v5 comparison; no GPS or inferred counterfactual vehicle response.",
+  "route": "route83",
+  "recorded_commit": "79a4caa1f6b71488949108aee9ae6ae6566347b1",
+  "fixture_sha256": "d00312c430ace47000c05b8284ee8d56df56ec24bb17a9ea8f4dce83133527c3",
+  "samples": 11744,
+  "model_count": 2367,
+  "source_cache_sha256": "53d786aff2e0b6338e1991320145305fda3101f7b76e50bd2929adbcaea95b28",
+  "response_delay": 0.20000000298023224,
+  "episodes": [
+    [
+      1861.2933736250002,
+      1874.756970279
+    ],
+    [
+      1878.07372132,
+      1892.07372132
+    ],
+    [
+      1950.874232409,
+      1964.874232409
+    ],
+    [
+      2440.9020600930003,
+      2456.964158177
+    ],
+    [
+      2580.722658577,
+      2611.364366768
+    ],
+    [
+      2734.478264791,
+      2764.574374172
+    ]
+  ],
+  "windows": [
+    {
+      "name": "successful_large_early",
+      "role": "authority_target",
+      "range_s": [
+        1866.722720383,
+        1874.756970279
+      ],
+      "samples": 426,
+      "substantial_demand_required": true,
+      "recorded_can_ratio_02s_median": 1.0233371460413845,
+      "published_median_abs_c0_c1": [
+        1.6002928018569946,
+        0.2796146124601364
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        1.6002928018569946,
+        0.2796146124601364
+      ],
+      "phase_samples": {
+        "phase_turn_in": 15,
+        "phase_held": 122,
+        "phase_release": 402,
+        "phase_reversal": 0
+      }
+    },
+    {
+      "name": "centering_reversal_positive_to_negative",
+      "role": "reversal",
+      "range_s": [
+        1888.07372132,
+        1892.07372132
+      ],
+      "samples": 396,
+      "substantial_demand_required": false,
+      "recorded_can_ratio_02s_median": null,
+      "published_median_abs_c0_c1": [
+        0.0,
+        0.0
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        0.0,
+        0.0
+      ],
+      "phase_samples": {
+        "phase_turn_in": 283,
+        "phase_held": 48,
+        "phase_release": 104,
+        "phase_reversal": 21
+      }
+    },
+    {
+      "name": "centering_reversal_negative_to_positive",
+      "role": "reversal",
+      "range_s": [
+        1960.874232409,
+        1964.874232409
+      ],
+      "samples": 397,
+      "substantial_demand_required": false,
+      "recorded_can_ratio_02s_median": null,
+      "published_median_abs_c0_c1": [
+        0.0,
+        0.0
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        0.0,
+        0.0
+      ],
+      "phase_samples": {
+        "phase_turn_in": 154,
+        "phase_held": 0,
+        "phase_release": 183,
+        "phase_reversal": 21
+      }
+    },
+    {
+      "name": "clean_release",
+      "role": "release",
+      "range_s": [
+        2453.714158177,
+        2456.964158177
+      ],
+      "samples": 323,
+      "substantial_demand_required": false,
+      "recorded_can_ratio_02s_median": null,
+      "published_median_abs_c0_c1": [
+        0.0,
+        0.0
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        0.0,
+        0.0
+      ],
+      "phase_samples": {
+        "phase_turn_in": 4,
+        "phase_held": 0,
+        "phase_release": 305,
+        "phase_reversal": 17
+      }
+    },
+    {
+      "name": "successful_smaller_positive",
+      "role": "sign_coverage_only",
+      "range_s": [
+        2590.722658577,
+        2600.918740146
+      ],
+      "samples": 175,
+      "substantial_demand_required": true,
+      "recorded_can_ratio_02s_median": 1.0960646334373787,
+      "published_median_abs_c0_c1": [
+        0.42173025012016296,
+        0.1222948431968689
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        0.42173025012016296,
+        0.1222948431968689
+      ],
+      "phase_samples": {
+        "phase_turn_in": 170,
+        "phase_held": 61,
+        "phase_release": 0,
+        "phase_reversal": 0
+      }
+    },
+    {
+      "name": "large_under_response",
+      "role": "under_response_challenge",
+      "range_s": [
+        2604.2254721,
+        2611.364366768
+      ],
+      "samples": 128,
+      "substantial_demand_required": true,
+      "recorded_can_ratio_02s_median": 0.7322859508492778,
+      "published_median_abs_c0_c1": [
+        2.4204851388931274,
+        0.42145511507987976
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        2.4204851388931274,
+        0.42145511507987976
+      ],
+      "phase_samples": {
+        "phase_turn_in": 68,
+        "phase_held": 96,
+        "phase_release": 56,
+        "phase_reversal": 0
+      }
+    },
+    {
+      "name": "successful_large_181deg",
+      "role": "authority_target",
+      "range_s": [
+        2744.478264791,
+        2750.573209708
+      ],
+      "samples": 207,
+      "substantial_demand_required": true,
+      "recorded_can_ratio_02s_median": 1.0087938914780248,
+      "published_median_abs_c0_c1": [
+        2.1044259071350098,
+        0.3815947473049164
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        2.1044259071350098,
+        0.3815947473049164
+      ],
+      "phase_samples": {
+        "phase_turn_in": 137,
+        "phase_held": 94,
+        "phase_release": 64,
+        "phase_reversal": 0
+      }
+    },
+    {
+      "name": "large_over_response_290deg",
+      "role": "over_response_challenge_not_target",
+      "range_s": [
+        2760.493612962,
+        2764.574374172
+      ],
+      "samples": 181,
+      "substantial_demand_required": true,
+      "recorded_can_ratio_02s_median": 1.2515789463064766,
+      "published_median_abs_c0_c1": [
+        4.737145900726318,
+        0.5235000252723694
+      ],
+      "send_clamped_median_abs_c0_c1": [
+        4.737145900726318,
+        0.5
+      ],
+      "phase_samples": {
+        "phase_turn_in": 139,
+        "phase_held": 90,
+        "phase_release": 41,
+        "phase_reversal": 0
+      }
+    }
+  ],
+  "selection": "Authority targets require automatic turn windows with >=1 second strict torque eligibility, eligible |wheel|>=150 degrees, and whole-window CAN response ratio median 0.90..1.10 at fixed 0.2 s. No positive-request large turn qualifies.",
+  "non_targets": "Positive smaller turn supplies sign coverage only. Under/over response and release/reversal windows are regression challenges, not authority targets.",
+  "context": "At least 10 s pre-roll or available route start, extended to include the preceding feedback reset/sign reversal. Overlapping intervals are merged. First episode begins at the partial route boundary with unobserved earlier history.",
+  "phase_policy": "Held means request curvature range over +/-0.25 s times speed squared <0.15 m/s2 at demand>=0.5. Turn-in/release compare current absolute curvature with the historical held request at measurement_time-delay, scaled by max(7,speed), using +/-0.0005 rad. These masks can overlap held; reversal means opposing delayed/current signs.",
+  "wire_policy": "Published coefficients preserve Float32 values. Send-clamped copy caps C0 to +/-5.11 and C1 to +/-0.5 before packing. Actual decoded wire is normalized to controller sign, nearest within 15 ms; wire_time/fresh/mode expose timing approximation.",
+  "model_schema": "models[model_index] contains position.x, position.y, orientation.z; Float32 conversion preserves the original model payload precision.",
+  "v5_reference": "Frozen full sequential replay from command_replay.npz, whose source hash and limitations are recorded in command_replay.json.",
+  "frozen_v5_revision": "09acf8ec2f327769f00ee53563ad2dd9225e37a7",
+  "preroll_validation": "Compact reset replay exactly matches full sequential frozen-v5 C0/C1, gates and bias on all 2233 evidence samples."
+}
@@ -0,0 +1,156 @@
+{
+  "description": "Anonymous recorded-input turn-exit regression fixture; command construction only, not simulated vehicle response.",
+  "baseline_revision": "dfcfddb91ce2409511f5b2dbce25d06d5056b3d6",
+  "baseline_hypothesis": "model-pose-c0-c1-feedback-v7",
+  "baseline_source_hashes": {
+    "controller_sha256": "4951a6352d89fcd66277bbfe682bd22e935a31b5a4db33e617ad21189b6705fd",
+    "allocator_sha256": "383538fc7cdae3bc28dffb71fe12ac5f3f9866ffbe6adfb7457f3593e9fc903a"
+  },
+  "fixture_sha256": "87a030c309061b7dc218715d05440c2077e465a8138079b46e8e8cee94201e54",
+  "source_fixture_sha256": "d476110b83dc628ffbd094220e464d6d3114b709bda2977813c3217964d41086",
+  "response_delay": 0.20000000298023224,
+  "publication_latency_estimate_s": 0.0015483515003040793,
+  "samples": 15273,
+  "model_count": 3078,
+  "evidence_samples": 4879,
+  "context_policy": "At least twenty seconds prior context, extended before the last observed reset. Overlapping intervals are merged.",
+  "provenance": "Selected from a recorded drive running the pinned baseline; request, model, driver and PSCM observations stay fixed during replay.",
+  "baseline_policy": "Stored commands, validity and bias exactly match the complete baseline replay on evidence samples. Context outside evidence initializes state and is not an exact-output target.",
+  "compact_full_baseline_evidence_parity": {
+    "commands": {
+      "exact": true,
+      "max_difference": 0.0
+    },
+    "valid": {
+      "exact": true,
+      "max_difference": 0.0
+    },
+    "heading_bias": {
+      "exact": true,
+      "max_difference": 0.0
+    }
+  },
+  "measurement_policy": "Controller computation time is estimated from publication time using the recorded median latency; exact vehicle motion under changed commands is unknown.",
+  "clean_policy": "Every sample from request time minus 0.5 s through plus 0.65 s is active, valid, fresh, unpressed and within 1 Nm raw driver torque. Demand is absolute desired curvature times current speed squared; substantial means at least 0.5 m/s2.",
+  "driver_policy": "All replay inputs retain driver interference; only comparison metrics use the clean mask. History-reset failures intentionally retain nearby driver context.",
+  "coordinates": "Elapsed seconds shifted to the first fixture control cycle; model x/y/heading are vehicle-relative, not global position.",
+  "retained_fields": [
+    "t",
+    "episode",
+    "model_index",
+    "models",
+    "desired_curvature",
+    "yaw_rate",
+    "speed",
+    "measurement_time",
+    "model_time",
+    "reference_time",
+    "active",
+    "valid",
+    "pressed",
+    "steering_torque",
+    "pscm_timestamp",
+    "pscm_valid",
+    "pscm_lateral_state",
+    "pscm_limit",
+    "pscm_capability",
+    "pscm_denied",
+    "clean_rawtorque",
+    "demand",
+    "window_masks",
+    "evidence",
+    "baseline_commands",
+    "baseline_valid",
+    "baseline_heading_base",
+    "baseline_heading_target",
+    "baseline_heading_bias",
+    "baseline_feedback_yaw_error",
+    "baseline_feedback_reference_curvature",
+    "baseline_status",
+    "baseline_offset_target"
+  ],
+  "omitted_data": "No route/device identifiers, VIN, GPS, private paths, raw wheel angle, wheel rate, EPS torque, or absolute clock origins.",
+  "baseline_status_meaning": "feedback_status from the pinned baseline",
+  "windows": [
+    {
+      "name": "good_curve_a",
+      "role": "comparison",
+      "range_s": [
+        20.0002130975003,
+        25.0002130975003
+      ],
+      "samples": 496,
+      "clean_substantial_samples": 259
+    },
+    {
+      "name": "first_reversal",
+      "role": "reversal",
+      "range_s": [
+        83.0002130975003,
+        92.7002130975003
+      ],
+      "samples": 964,
+      "clean_substantial_samples": 167
+    },
+    {
+      "name": "good_curve_b",
+      "role": "comparison",
+      "range_s": [
+        121.0002130975003,
+        128.0002130975003
+      ],
+      "samples": 695,
+      "clean_substantial_samples": 308
+    },
+    {
+      "name": "second_reversal",
+      "role": "reversal",
+      "range_s": [
+        133.5002130975003,
+        138.9002130975003
+      ],
+      "samples": 537,
+      "clean_substantial_samples": 191
+    },
+    {
+      "name": "large_turn_driver_context_a",
+      "role": "driver_context",
+      "range_s": [
+        150.0002130975003,
+        157.0002130975003
+      ],
+      "samples": 695,
+      "clean_substantial_samples": 0
+    },
+    {
+      "name": "over_growth",
+      "role": "over_response",
+      "range_s": [
+        182.0002130975003,
+        191.0002130975003
+      ],
+      "samples": 897,
+      "clean_substantial_samples": 66
+    },
+    {
+      "name": "large_turn_driver_context_b",
+      "role": "driver_context",
+      "range_s": [
+        199.0002130975003,
+        205.0002130975003
+      ],
+      "samples": 595,
+      "clean_substantial_samples": 281
+    },
+    {
+      "name": "zero_bias_release",
+      "role": "under_response",
+      "range_s": [
+        202.0002130975003,
+        205.0002130975003
+      ],
+      "samples": 297,
+      "clean_substantial_samples": 279
+    }
+  ]
+}
@@ -0,0 +1,59 @@
+import ast
+import io
+import json
+import logging
+from pathlib import Path
+from types import SimpleNamespace
+import unittest
+
+from openpilot.common.logging_extra import SwagFormatter, SwagLogger
+from openpilot.selfdrive.controls.lib.ford_model_action import FordModelActionController
+from openpilot.selfdrive.controls.lib.ford_path import FordPathController, FordPscmObserverPathController
+from openpilot.selfdrive.controls.tests.test_ford_model_action import circle
+
+
+class TestFordControlsLogging(unittest.TestCase):
+  def emit_controls_event(self, event, controls):
+    # Execute the actual controlsd call with the real logger and formatter,
+    # without launching hardware-dependent Controls or opening logging IPC.
+    source_path = Path(__file__).resolve().parents[1] / 'controlsd.py'
+    source = ast.parse(source_path.read_text())
+    calls = [node for node in ast.walk(source) if isinstance(node, ast.Call)
+             and isinstance(node.func, ast.Attribute) and isinstance(node.func.value, ast.Name)
+             and node.func.value.id == 'cloudlog' and node.args
+             and isinstance(node.args[0], ast.Constant) and node.args[0].value == event]
+    self.assertEqual(len(calls), 1)
+    logger = SwagLogger()
+    logger.setLevel(logging.INFO)  # disabled INFO logging would hide this crash
+    stream = io.StringIO()
+    handler = logging.StreamHandler(stream)
+    handler.setFormatter(SwagFormatter(logger))
+    logger.addHandler(handler)
+    try:
+      expression = ast.Expression(body=calls[0])
+      eval(compile(expression, str(source_path), 'eval'), {'cloudlog': logger, 'self': controls, 'reference_service': 'modelV2'})
+      record = json.loads(stream.getvalue())
+    finally:
+      handler.close()
+    self.assertEqual(record['level'], 'INFO')
+    self.assertEqual(record['msg']['event'], event)
+    return record['msg']
+
+  def test_startup_logs_selected_controller_without_crashing(self):
+    for controller in (FordPathController(), FordPscmObserverPathController(), FordModelActionController()):
+      with self.subTest(controller=type(controller).__name__):
+        record = self.emit_controls_event('Ford path controller selected', SimpleNamespace(ford_path_controller=controller))
+        self.assertEqual(record['controller'], type(controller).__name__)
+
+  def test_candidate_diagnostics_identify_the_experiment_and_do_not_claim_calibration(self):
+    controller = FordModelActionController()
+    for active, valid in ((False, True), (True, True), (True, False)):
+      controller.update(circle(.01), .005, current_curvature=.0025, yaw_rate=.05, speed=20., now=1.,
+                        measurement_time=1., model_time=1., reference_time=1., active=active, valid=valid)
+      controls = SimpleNamespace(ford_path_controller=controller, desired_curvature=.005, curvature=.0025,
+                                 sm=SimpleNamespace(logMonoTime={'modelV2': 123456789, 'carState': 123450000}))
+      record = self.emit_controls_event('Ford C2-free path tracking', controls)
+      self.assertEqual(record['hypothesis'], 'model-action-c1-feedback-v1')
+      self.assertIs(record['calibration_approved'], False)
+      self.assertEqual(record['command'][2:], [0., 0.])
+      self.assertEqual(record['status'], controller.diagnostics['status'])
@@ -0,0 +1,193 @@
+import math
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from opendbc.can import CANPacker, CANParser
+from opendbc.car.ford.fordcan import CanBus, create_lat_ctl2_msg
+from openpilot.cereal import custom
+from openpilot.selfdrive.controls.lib.ford_path import FordPath
+from openpilot.selfdrive.controls.lib.ford_model_action import ModelActionController, encode_model_action
+
+
+def make_model(x, y, heading):
+  return SimpleNamespace(position=SimpleNamespace(x=x, y=y), orientation=SimpleNamespace(z=heading))
+
+
+def circle(curvature):
+  s = np.linspace(0., 60., 601)
+  return make_model(np.sin(curvature*s)/curvature, (1-np.cos(curvature*s))/curvature, curvature*s)
+
+
+def straight(offset=0.):
+  x = np.linspace(0., 60., 121)
+  return make_model(x, np.full_like(x, offset), np.zeros_like(x))
+
+
+def test_selected_action_controls_heading_even_when_model_previews_another_turn():
+  model = circle(.02)
+  assert encode_model_action(model, 0., 20.).path_angle == 0.
+  assert encode_model_action(model, -.004, 20.).path_angle == pytest.approx(-.08)
+  assert encode_model_action(model, 0., 20.).path_offset > 0.
+
+
+def test_centering_information_is_independent_of_action_and_not_scaled_with_speed():
+  for speed in (2., 7., 20., 35.):
+    target = encode_model_action(straight(.4), 0., speed)
+    assert target == FordPath(True, .4, 0., 0., 0.)
+  for sign in (-1, 1):
+    target = encode_model_action(circle(sign*.01), sign*.01, 20.)
+    assert target.path_offset == pytest.approx(sign*(1-math.cos(.07))/.01, abs=1e-6)
+    assert target.path_angle == pytest.approx(sign*.2)  # No 10 m cap at highway speed.
+
+
+def test_three_control_states_are_sufficient_for_every_next_output():
+  controller = ModelActionController()
+  assert not hasattr(controller, '__dict__')
+  for i in range(300):
+    copied = ModelActionController()
+    copied.c0, copied.c1, copied.correction = controller.c0, controller.c1, controller.correction
+    model = straight(.2*math.sin(i*.1))
+    kwargs = {'speed': 20., 'dt': .01}
+    desired = .005*math.cos(i*.03)
+    assert controller.update(model, desired, current_curvature=0., **kwargs) == copied.update(model, desired, current_curvature=0., **kwargs)
+
+
+def test_held_turn_releases_without_a_bias_tail_or_sign_reversal():
+  for sign in (-1., 1.):
+    controller = ModelActionController()
+    for _ in range(400):
+      out = controller.update(circle(sign*.01), sign*.01, current_curvature=sign*.01, speed=20., dt=.01)
+    assert out.path_angle == pytest.approx(sign*.2)
+    previous = np.array([out.path_offset, out.path_angle])
+    for desired in sign*np.linspace(.01, 0., 101):
+      out = controller.update(straight(), desired, current_curvature=desired, speed=20., dt=.01)
+      values = np.array([out.path_offset, out.path_angle])
+      assert (abs(values) <= abs(previous)+1e-8).all()
+      assert (sign*values >= -1e-8).all()
+      previous = values
+    assert out == FordPath(True, 0., 0., 0., 0.)
+
+
+def test_current_model_replacement_leaves_only_independent_actuator_slew():
+  controller = ModelActionController()
+  for _ in range(150):
+    controller.update(straight(1.), .04, current_curvature=.04, speed=20., dt=.01)
+  for _ in range(25):
+    out = controller.update(straight(), 0., current_curvature=0., speed=20., dt=.01)
+  assert out.path_offset == pytest.approx(0.)
+  assert out.path_angle > 0.  # C1 cannot hold C0 during its longer release.
+  for _ in range(75):
+    out = controller.update(straight(), 0., current_curvature=0., speed=20., dt=.01)
+  assert out == FordPath(True, 0., 0., 0., 0.)
+
+
+@pytest.mark.parametrize('overrides', [{'active': False}, {'valid': False}, {'dt': .2}, {'speed': math.nan}])
+def test_invalid_or_inactive_input_clears_state_before_reengagement(overrides):
+  controller = ModelActionController()
+  for _ in range(100):
+    controller.update(straight(.5), .01, current_curvature=.01, speed=20., dt=.01)
+  kwargs = {'speed': 20., 'dt': .01, 'active': True, 'valid': True}
+  kwargs.update(overrides)
+  assert controller.update(straight(), 0., current_curvature=0., **kwargs) == FordPath()
+  assert (controller.c0, controller.c1) == (0., 0.)
+  assert controller.update(straight(), 0., current_curvature=0., speed=20., dt=.01) == FordPath(True, 0., 0., 0., 0.)
+
+
+def test_malformed_geometry_and_nonfinite_action_never_create_an_active_command():
+  for model, desired in ((None, 0.), (straight(), math.nan), (straight(), math.inf)):
+    assert not encode_model_action(model, desired, 20.).valid
+
+
+def test_selected_core_reversal_through_float32_and_wire_keeps_sign_and_zero_c2():
+  controller = ModelActionController()
+  packer = CANPacker('ford_lincoln_base_pt')
+  parser = CANParser('ford_lincoln_base_pt', [('LateralMotionControl2', 100)], 0)
+  bus = CanBus(fingerprint={0: {}})
+  previous = np.zeros(2)
+  for i in range(600):
+    sign = 1. if i < 300 else -1.
+    out = controller.update(straight(sign*8.), sign*.1, current_curvature=sign*.1, speed=30., dt=.01)
+    fields = np.array([out.path_offset, out.path_angle])
+    assert (abs(fields) <= [5.1100001, .5000001]).all()
+    assert (abs(fields-previous) <= [.0500001, .0055001]).all()
+    previous = fields
+    message = custom.CarControlSP.new_message()
+    message.fordLateralPath.pathOffset = out.path_offset
+    message.fordLateralPath.pathAngle = out.path_angle
+    packet = create_lat_ctl2_msg(packer, bus, 2, -message.fordLateralPath.pathOffset,
+                                -message.fordLateralPath.pathAngle, out.curvature, out.curvature_rate, i % 16)
+    parser.update([i*10_000_000, [packet]])
+    decoded = parser.vl['LateralMotionControl2']
+    assert decoded['LatCtlPathOffst_L_Actl'] == pytest.approx(-out.path_offset)
+    assert decoded['LatCtlPath_An_Actl'] == pytest.approx(-out.path_angle)
+    assert decoded['LatCtlCurv_No_Actl'] == decoded['LatCtlCrv_NoRate2_Actl'] == 0.
+
+
+def test_short_path_holds_available_endpoint_without_extrapolation():
+  model = make_model([0., 1.], [0., .1], [0., 0.])
+  assert encode_model_action(model, .01, 20.) == FordPath(True, .1, .2, 0., 0.)
+
+
+def test_overflowing_arc_resets_instead_of_publishing_invalid_geometry():
+  model = make_model([0., 1e308, -1e308], [0., 0., 0.], [0., 0., 0.])
+  controller = ModelActionController()
+  controller.update(straight(.4), .01, current_curvature=.01, speed=20., dt=.01)
+  assert controller.update(model, .01, current_curvature=.01, speed=20., dt=.01) == FordPath()
+  assert (controller.c0, controller.c1) == (0., 0.)
+
+
+@pytest.mark.parametrize('value', [None, 'bad', 10**400])
+@pytest.mark.parametrize('field', ['dt', 'speed', 'desired_curvature'])
+def test_malformed_numeric_input_resets_without_throwing(field, value):
+  controller = ModelActionController()
+  kwargs = {'speed': 20., 'dt': .01, 'desired_curvature': .01}
+  controller.update(straight(.4), current_curvature=kwargs['desired_curvature'], **kwargs)
+  kwargs[field] = value
+  assert controller.update(straight(.4), current_curvature=kwargs['desired_curvature'], **kwargs) == FordPath()
+  assert (controller.c0, controller.c1) == (0., 0.)
+
+
+@pytest.mark.parametrize('model', [
+  make_model([], [], []), make_model([0.], [0.], [0.]),
+  make_model([0., 10.], [0.], [0., 0.]), make_model([0., 10.], [0., 0.], [0.]),
+  make_model([0., 0.], [0., 0.], [0., 0.]),
+  make_model([0., 10.], [0., math.nan], [0., 0.]), make_model([0., math.inf], [0., 0.], [0., 0.]),
+  make_model([0., 10.], [0., 0.], [0., math.inf]),
+  make_model([0., 10**400], [0., 0.], [0., 0.]),
+  make_model([0., 10.], [0., 0.], [1e308, -1e308]),
+])
+def test_malformed_model_arrays_cannot_reuse_a_previous_valid_command(model):
+  controller = ModelActionController()
+  controller.update(straight(.4), .01, current_curvature=.01, speed=20., dt=.01)
+  assert controller.update(model, .01, current_curvature=.01, speed=20., dt=.01) == FordPath()
+  assert (controller.c0, controller.c1) == (0., 0.)
+
+
+@pytest.mark.parametrize('field,value,valid', [
+  ('speed', .2999, False), ('speed', .3, True), ('speed', 55., True), ('speed', 55.0001, False),
+  ('desired_curvature', -1., True), ('desired_curvature', 1., True), ('desired_curvature', -1.0001, False),
+  ('dt', .001999, False), ('dt', .002, True), ('dt', .1, True), ('dt', .100001, False), ('dt', 0., False),
+])
+def test_domain_and_elapsed_time_boundaries(field, value, valid):
+  kwargs = {'speed': 20., 'desired_curvature': .01, 'dt': .01}
+  kwargs[field] = value
+  assert ModelActionController().update(straight(.4), current_curvature=kwargs['desired_curvature'], **kwargs).valid == valid
+
+
+def test_arc_station_not_forward_x_or_model_heading_determines_offset():
+  x = np.array([0., 6., 12.])
+  y = .4+x*.75
+  target = encode_model_action(make_model(x, y, [2., -2., 1.]), -.01, 20.)
+  # Arc length is 1.25*x on this line, so y(arc=7)=.4+.75*(7/1.25).
+  assert target.path_offset == pytest.approx(4.6)
+  assert target.path_angle == pytest.approx(-.2)
+
+
+def test_duplicate_stations_keep_valid_geometry_and_first_cycle_slew():
+  model = make_model([0., 0., 10.], [.4, .4, .4], [0., 0., 0.])
+  assert encode_model_action(model, .01, 20.) == FordPath(True, .4, .2, 0., 0.)
+  out = ModelActionController().update(model, .01, current_curvature=.01, speed=20., dt=.002)
+  assert out.path_offset == pytest.approx(.01)
+  assert out.path_angle == pytest.approx(.001)
@@ -0,0 +1,291 @@
+"""Exercise the candidate through existing selection, publication and CAN code.
+
+Tests enable the candidate through controlsd's real startup selection.
+No hardware, IPC or CAN transmission is involved.
+"""
+import ast
+from collections import defaultdict
+import json
+import math
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from opendbc.can import CANParser
+from opendbc.car import Bus, structs
+from opendbc.car.ford.carcontroller import CarController
+from opendbc.car.ford.fordcan import calculate_lat_ctl2_checksum
+from opendbc.car.ford.values import FordFlags
+from openpilot.cereal import custom
+from openpilot.selfdrive.car.helpers import convert_carControlSP
+from openpilot.selfdrive.controls.lib.drive_helpers import clip_curvature
+from openpilot.selfdrive.controls.lib.ford_model_action import FordModelActionController
+from openpilot.selfdrive.controls.lib.ford_path import FordPath
+from openpilot.selfdrive.controls.tests.test_ford_model_action import circle, straight
+from openpilot.selfdrive.controls.tests.test_ford_model_action_selection import startup
+
+
+def update(controller, now=1., **overrides):
+  kwargs = {'model': straight(.4), 'desired_curvature': .01, 'speed': 20., 'yaw_rate': 0., 'now': now,
+                'model_time': now, 'measurement_time': now, 'reference_time': now, 'active': True}
+  kwargs.update(overrides)
+  kwargs.setdefault('current_curvature', kwargs['desired_curvature'])  # Preserve feedforward-only compatibility probes.
+  return controller.update(**kwargs)
+
+
+@pytest.mark.parametrize('field', ['model_time', 'measurement_time', 'reference_time'])
+@pytest.mark.parametrize('age', [.151, -.006])
+def test_stale_or_future_service_clears_commands_and_reengages_from_zero(field, age):
+  controller = FordModelActionController()
+  update(controller)
+  assert update(controller, 1.01, **{field: 1.01-age}) == FordPath()
+  assert controller.diagnostics['status'] == 'stale_input'
+  assert update(controller, 1.02).path_offset == pytest.approx(.04)
+
+
+@pytest.mark.parametrize('change,reason', [
+  ({'now': 1.}, 'timing_reset'),
+  ({'now': .99}, 'timing_reset'),
+  ({'now': 1.001}, 'timing_reset'),
+  ({'now': 1.101}, 'timing_reset'),
+  ({'model_time': .999}, 'timing_reset'),
+  ({'measurement_time': .999}, 'timing_reset'),
+  ({'active': False}, 'inactive'),
+  ({'valid': False}, 'invalid_service'),
+  ({'model': None}, 'invalid_path'),
+  ({'yaw_rate': math.nan}, 'nonfinite'),
+  ({'yaw_rate': 3.01}, 'input_range'),
+  ({'speed': 55.01}, 'input_range'),
+  ({'desired_curvature': 1.01}, 'input_range'),
+])
+def test_invalid_cycle_never_keeps_a_previous_active_request(change, reason):
+  controller = FordModelActionController()
+  update(controller)
+  now = change.get('now', 1.01)
+  assert update(controller, **dict(change, now=now)) == FordPath()
+  assert controller.diagnostics['status'] == reason
+  assert (controller.core.c0, controller.core.c1) == (0., 0.)
+  assert update(controller, now+1.).path_angle == pytest.approx(.005)
+
+
+@pytest.mark.parametrize('field', ['now', 'measurement_time', 'model_time', 'reference_time', 'speed', 'yaw_rate', 'desired_curvature'])
+@pytest.mark.parametrize('value', [math.nan, math.inf, -math.inf, None])
+def test_nonfinite_input_never_raises_or_leaks_into_diagnostics(field, value):
+  controller = FordModelActionController()
+  update(controller)
+  assert update(controller, **{field: value}) == FordPath()
+  assert controller.diagnostics['status'] == 'nonfinite'
+  json.dumps(controller.diagnostics, allow_nan=False)
+
+
+def test_repeated_measurements_do_not_freeze_slew_or_cache_invalid_model_geometry():
+  controller = FordModelActionController()
+  for i in range(10):
+    result = update(controller, 1.+i*.01, measurement_time=1., model_time=1., reference_time=1.)
+  assert result.path_offset == pytest.approx(.4)
+  assert result.path_angle == pytest.approx(.05)
+  broken = straight(.4)
+  broken.position.y[5] = math.nan
+  assert update(controller, 1.1, model=broken, model_time=1., measurement_time=1.) == FordPath()
+  assert controller.diagnostics['status'] == 'invalid_path'
+
+
+def test_yaw_offset_does_not_change_the_base():
+  controllers = [FordModelActionController() for _ in range(3)]
+  variants = [{}, {'yaw_rate': .0072}, {'yaw_rate': -.0072}]
+  for i in range(100):
+    outputs = [update(c, 1.+i*.01, **kwargs) for c, kwargs in zip(controllers, variants, strict=True)]
+    assert all(out == outputs[0] for out in outputs)
+  assert outputs[0].path_angle == pytest.approx(.2)
+
+
+def test_reference_source_can_change_to_an_older_but_fresh_publication():
+  controller = FordModelActionController()
+  update(controller, reference_time=.99)
+  assert update(controller, 1.01, reference_time=.98).valid
+
+
+def test_release_keeps_current_geometry_and_may_grow_c0_while_c1_decreases():
+  for sign in (-1., 1.):
+    controller = FordModelActionController()
+    for i in range(100):
+      before = update(controller, 1.+i*.01, model=circle(sign*.01), desired_curvature=sign*.005)
+    for i in range(100):
+      after = update(controller, 2.+i*.01, model=circle(sign*.02), desired_curvature=sign*.004)
+    assert abs(after.path_offset) > abs(before.path_offset)
+    assert abs(after.path_angle) < abs(before.path_angle)
+    for i in range(100):
+      released = update(controller, 3.+i*.01, model=circle(sign*.02), desired_curvature=0.)
+    assert released.path_offset == after.path_offset
+    assert released.path_angle == pytest.approx(0.)
+
+
+def _method(filename, class_name, method):
+  tree = ast.parse(filename.read_text())
+  cls = next(node for node in tree.body if isinstance(node, ast.ClassDef) and node.name == class_name)
+  return next(node for node in cls.body if isinstance(node, ast.FunctionDef) and node.name == method)
+
+
+@pytest.fixture
+def pipeline():
+  root = Path(__file__).resolve().parents[3]
+  controls_file = root/'selfdrive/controls/controlsd.py'
+  body = _method(controls_file, 'Controls', 'state_control').body
+  # Execute the actual source choice, upstream limiter and Ford integration.
+  selection = next(n for n in body if isinstance(n, ast.If) and ast.unparse(n.test) == "self.sm.valid['lateralManeuverPlan']")
+  limiter = next(n for n in body if isinstance(n, ast.Assign) and isinstance(n.value, ast.Call) and
+                 isinstance(n.value.func, ast.Name) and n.value.func.id == 'clip_curvature')
+  branch = next(n for n in body if isinstance(n, ast.If) and ast.unparse(n.test) == "self.CP.brand == 'ford'")
+  call = compile(ast.Module(body=[selection, limiter, branch], type_ignores=[]), str(controls_file), 'exec')
+  publication_file = root/'sunnypilot/selfdrive/controls/controlsd_ext.py'
+  body = _method(publication_file, 'ControlsExt', 'state_control_ext').body
+  publish = [n for n in body if (isinstance(n, ast.Assign) and ast.unparse(n.targets[0]) == 'ford_path') or
+             (isinstance(n, ast.If) and ast.unparse(n.test) == 'ford_path is not None')]
+  assert len(publish) == 2
+  publication = compile(ast.Module(body=publish, type_ignores=[]), str(publication_file), 'exec')
+  return call, publication
+
+
+class Subscriptions:
+  frame = 1
+
+  def __init__(self, maneuver):
+    self.valid = {'lateralManeuverPlan': maneuver, 'modelV2': True, 'carStateSP': True}
+    self.logMonoTime = {'carState': 995_000_000, 'modelV2': 980_000_000, 'lateralManeuverPlan': 990_000_000}
+    self.failed = set()
+    self.messages = {'carStateSP': custom.CarStateSP.new_message(), 'lateralManeuverPlan': SimpleNamespace(desiredCurvature=-.1)}
+
+  def __getitem__(self, service):
+    return self.messages[service]
+
+  def all_checks(self, services):
+    return not self.failed.intersection(services) and all(self.valid.get(s, True) for s in services)
+
+
+@pytest.mark.parametrize('maneuver', [False, True])
+def test_actual_controlsd_selection_limiting_publication_and_downstream_can(pipeline, maneuver):
+  call, publication = pipeline
+  sm = Subscriptions(maneuver)
+  controls = startup()
+  controller = controls.ford_path_controller
+  controls.sm, controls.desired_curvature, controls.curvature = sm, 0., 0.
+  model = straight(.4)
+  model.action = SimpleNamespace(desiredCurvature=.1)
+  cc = structs.CarControl(latActive=True)
+  cs = SimpleNamespace(vEgo=20., yawRate=-.0072, canValid=True, steeringPressed=False, steeringTorque=0.)
+  environment = {'self': controls, 'CS': cs, 'CC': cc, 'actuators': cc.actuators, 'model_v2': model, 'lp': SimpleNamespace(roll=0.),
+                     'clip_curvature': clip_curvature, 'time': SimpleNamespace(monotonic=lambda: 1.)}
+  exec(call, environment)
+  expected_curvature = (-1 if maneuver else 1)*.000125
+  assert controls.desired_curvature == pytest.approx(expected_curvature)
+  assert controls.ford_path.path_angle == pytest.approx(20.*expected_curvature)
+  assert controls.ford_path.path_offset == pytest.approx(.04)
+  assert cc.latActive and cc.actuators.curvature == 0.
+  assert controller.diagnostics['reference_age'] == pytest.approx(.01 if maneuver else .02)
+
+  cp = structs.CarParams(flags=int(FordFlags.CANFD), carFingerprint='FORD_F_150_LIGHTNING_MK1')
+  downstream = CarController({Bus.pt: 'ford_lincoln_base_pt'}, cp, structs.CarParamsSP())
+  vehicle = SimpleNamespace(out=structs.CarState(vEgo=20., vEgoRaw=20.), acc_tja_status_stock_values=defaultdict(int),
+                            lkas_status_stock_values=defaultdict(int), buttons_stock_values=defaultdict(int))
+  parser = CANParser('ford_lincoln_base_pt', [('LateralMotionControl2', 100)], downstream.CAN.main)
+  for i, fail in enumerate((False, True)):
+    if fail:
+      sm.failed.add('modelV2')
+      exec(call, environment)
+      assert not cc.latActive and controls.ford_path == FordPath()
+    msg = custom.CarControlSP.new_message()
+    exec(publication, {'self': controls, 'CC_SP': msg})
+    _, packets = downstream.update(cc.as_reader(), convert_carControlSP(msg.as_reader()), vehicle, (i+1)*10_000_000)
+    parser.update([(i+1)*10_000_000, packets])
+    wire = parser.vl['LateralMotionControl2']
+    assert wire['LatCtlPathOffst_L_Actl'] == pytest.approx(-controls.ford_path.path_offset)
+    assert wire['LatCtlPath_An_Actl'] == pytest.approx(-controls.ford_path.path_angle)
+    assert wire['LatCtlCurv_No_Actl'] == wire['LatCtlCrv_NoRate2_Actl'] == 0.
+    assert wire['LatCtl_D2_Rq'] == (0 if fail else 2)
+
+
+@pytest.mark.parametrize('maneuver', [False, True])
+@pytest.mark.parametrize('failed', ['carState', 'modelV2', 'vehicleParameters', 'lateralManeuverPlan'])
+def test_actual_controlsd_service_gates(pipeline, maneuver, failed):
+  sm = Subscriptions(maneuver)
+  sm.failed.add(failed)
+  controls = startup()
+  controls.sm, controls.desired_curvature, controls.curvature = sm, 0., 0.
+  cc = structs.CarControl(latActive=True)
+  cs = SimpleNamespace(vEgo=20., yawRate=0., canValid=True, steeringPressed=False, steeringTorque=0.)
+  model = straight()
+  model.action = SimpleNamespace(desiredCurvature=.1)
+  exec(pipeline[0], {'self': controls, 'CS': cs, 'CC': cc, 'actuators': cc.actuators, 'model_v2': model, 'lp': SimpleNamespace(roll=0.),
+                         'clip_curvature': clip_curvature, 'time': SimpleNamespace(monotonic=lambda: 1.)})
+  assert controls.ford_path.valid == cc.latActive == (failed == 'lateralManeuverPlan' and not maneuver)
+
+
+@pytest.mark.parametrize('sign', [-1., 1.])
+def test_feedback_through_actual_controlsd_publication_and_100hz_sender(pipeline, sign):
+  call, publication = pipeline
+  controls, sm = startup(), Subscriptions(False)
+  controls.sm, controls.desired_curvature = sm, sign*.004
+  model = straight(.4)
+  model.action = SimpleNamespace(desiredCurvature=sign*.004)
+  cc = structs.CarControl(latActive=True)
+  cs = SimpleNamespace(vEgo=20., yawRate=.2, canValid=True, steeringPressed=False, steeringTorque=0.)
+  cp = structs.CarParams(flags=int(FordFlags.CANFD), carFingerprint='FORD_F_150_LIGHTNING_MK1')
+  downstream = CarController({Bus.pt: 'ford_lincoln_base_pt'}, cp, structs.CarParamsSP())
+  vehicle = SimpleNamespace(out=structs.CarState(vEgo=20., vEgoRaw=20.), acc_tja_status_stock_values=defaultdict(int),
+                            lkas_status_stock_values=defaultdict(int), buttons_stock_values=defaultdict(int))
+  parser = CANParser('ford_lincoln_base_pt', [('LateralMotionControl2', 100)], downstream.CAN.main)
+  frame = 0
+  for measured, torque, count, expected in [(sign*.004, 0., 100, 0.), (sign*.003, 0., 100, sign*.02),
+                                           (sign*.004, 0., 100, sign*.02), (sign*.005, 0., 100, 0.),
+                                           (sign*.003, 0., 100, sign*.02), (0., 1.0625, 5, 0.)]:
+    for _ in range(count):
+      now = 1.+frame*.01
+      controls.curvature, cs.steeringTorque = measured, torque
+      sm.logMonoTime.update(carState=round(now*1e9), modelV2=round(now*1e9))
+      environment = {'self': controls, 'CS': cs, 'CC': cc, 'actuators': cc.actuators, 'model_v2': model,
+                     'lp': SimpleNamespace(roll=0.), 'clip_curvature': clip_curvature,
+                     'time': SimpleNamespace(monotonic=lambda now=now: now)}
+      exec(call, environment)
+      msg = custom.CarControlSP.new_message()
+      exec(publication, {'self': controls, 'CC_SP': msg})
+      _, packets = downstream.update(cc.as_reader(), convert_carControlSP(msg.as_reader()), vehicle, round(now*1e9))
+      received = parser.update([round(now*1e9), packets])
+      assert parser.dbc.name_to_msg['LateralMotionControl2'].address in received
+      wire = parser.vl['LateralMotionControl2']
+      assert wire['LatCtlPath_An_Actl'] == pytest.approx(-controls.ford_path.path_angle)
+      assert wire['LatCtlPathOffst_L_Actl'] == pytest.approx(-controls.ford_path.path_offset)
+      assert wire['LatCtlCurv_No_Actl'] == wire['LatCtlCrv_NoRate2_Actl'] == 0.
+      assert wire['LatCtl_D2_Rq'] == 2
+      assert wire['LatCtlPath_No_Cnt'] == frame % 16
+      address = parser.dbc.name_to_msg['LateralMotionControl2'].address
+      packet = next(packet for packet in packets if packet[0] == address)
+      assert wire['LatCtlPath_No_Cs'] == calculate_lat_ctl2_checksum(2, frame % 16, packet[1])
+      frame += 1
+    assert controls.ford_path_controller.core.correction == pytest.approx(expected)
+    assert controls.ford_path.path_angle == pytest.approx(sign*.08+expected)
+    assert controls.ford_path.path_offset == pytest.approx(.4)
+
+
+@pytest.mark.parametrize('service_valid', [False, True])
+def test_actual_controlsd_passes_only_valid_pscm_service_to_feedback(pipeline, service_valid):
+  controls, sm = startup(), Subscriptions(False)
+  controls.sm, controls.desired_curvature = sm, .004
+  model = straight(.4)
+  model.action = SimpleNamespace(desiredCurvature=.004)
+  cc = structs.CarControl(latActive=True)
+  cs = SimpleNamespace(vEgo=20., yawRate=0., canValid=True, steeringPressed=False, steeringTorque=0.)
+  for frame in range(101):
+    now = 1.+frame*.01
+    controls.curvature = .004 if frame < 100 else .003
+    sm.logMonoTime.update(carState=round(now*1e9), modelV2=round(now*1e9))
+    sm.valid['carStateSP'] = service_valid
+    status = sm['carStateSP'].fordPscmStatus
+    status.valid, status.canMonoTime, status.limit, status.lateralState = True, round(now*1e9), 2, 2
+    exec(pipeline[0], {'self': controls, 'CS': cs, 'CC': cc, 'actuators': cc.actuators, 'model_v2': model,
+                      'lp': SimpleNamespace(roll=0.), 'clip_curvature': clip_curvature,
+                      'time': SimpleNamespace(monotonic=lambda now=now: now)})
+  controller = controls.ford_path_controller
+  assert controller.diagnostics['pscm_limited'] is service_valid
+  assert controller.core.correction == pytest.approx(0. if service_valid else .0002)
+  assert cc.latActive and controls.ford_path.valid
@@ -0,0 +1,182 @@
+"""C1 feedback behavior; these tests do not simulate a Ford steering plant."""
+import math
+from types import SimpleNamespace
+
+import pytest
+
+from openpilot.selfdrive.controls.lib.ford_model_action import FordModelActionController, ModelActionController
+from openpilot.selfdrive.controls.lib.ford_path import FordPath
+from openpilot.selfdrive.controls.tests.test_ford_model_action import straight
+
+
+def tick(controller, desired, measured, **overrides):
+  kwargs = {'current_curvature': measured, 'speed': 20., 'dt': .01}
+  kwargs.update(overrides)
+  return controller.update(straight(.4), desired, **kwargs)
+
+
+@pytest.mark.parametrize('sign', [-1., 1.])
+def test_feedback_builds_holds_and_unwinds_without_changing_c0(sign):
+  controller, matched = ModelActionController(), ModelActionController()
+  for _ in range(100):
+    tick(controller, sign*.004, sign*.004)
+  for _ in range(100):
+    out = tick(controller, sign*.004, sign*.003)
+    baseline = tick(matched, sign*.004, sign*.004)
+  assert controller.correction == pytest.approx(sign*.02)
+  assert out.path_angle == pytest.approx(sign*.1)
+  assert out.path_offset == baseline.path_offset == pytest.approx(.4)
+  for _ in range(100):
+    out = tick(controller, sign*.004, sign*.004)
+  assert controller.correction == pytest.approx(sign*.02)
+  assert out.path_angle == pytest.approx(sign*.1)
+  for _ in range(200):
+    out = tick(controller, sign*.004, sign*.005)
+  assert controller.correction == pytest.approx(-sign*.02)
+  assert out.path_angle == pytest.approx(sign*.06)
+  assert out.curvature == out.curvature_rate == 0.
+
+
+@pytest.mark.parametrize('sign', [-1., 1.])
+def test_amplitude_and_slew_limits_do_not_store_unavailable_feedback(sign):
+  controller = ModelActionController()
+  # The unchanged model request is already ahead of the output slew.
+  for _ in range(10):
+    tick(controller, sign*.01, 0.)
+    assert controller.correction == 0.
+  for _ in range(1000):
+    before = controller.c1
+    tick(controller, sign*.01, -sign*.9)
+    assert abs(controller.c1-before) <= .0050000001
+    assert abs(controller.correction) <= .3000000001
+  assert controller.c1 == pytest.approx(sign*.5)
+  assert controller.correction == pytest.approx(sign*.3)
+  for _ in range(200):
+    tick(controller, sign*.01, 0.)
+  assert controller.correction == pytest.approx(sign*.3)
+  tick(controller, sign*.01, sign*.02)
+  assert sign*controller.correction < .3  # Unwind is allowed at the cap.
+  assert sign*controller.c1 < .5
+
+
+@pytest.mark.parametrize('sign', [-1., 1.])
+def test_pscm_limit_only_blocks_feedback_further_into_measured_turn(sign):
+  controller = ModelActionController()
+  for _ in range(100):
+    tick(controller, sign*.004, sign*.004)
+  for _ in range(100):
+    tick(controller, sign*.004, sign*.003, pscm_limited=True)
+  assert controller.correction == 0.
+  out = tick(controller, sign*.004, sign*.005, pscm_limited=True)
+  assert sign*controller.correction < 0.
+  # A limit cannot stall the new model request itself or its unwind slew.
+  for _ in range(100):
+    out = tick(controller, 0., 0., pscm_limited=True)
+  assert abs(out.path_angle) < .001
+  assert out.path_offset == pytest.approx(.4)
+
+
+@pytest.mark.parametrize('sign', [-1., 1.])
+def test_pscm_limit_cannot_trap_old_correction_below_the_model_request(sign):
+  controller = ModelActionController()
+  controller.correction = -sign*.02
+  controller.c1 = sign*.06
+  for _ in range(200):
+    out = tick(controller, sign*.004, sign*.003, pscm_limited=True)
+  assert controller.correction == pytest.approx(0.)
+  assert out.path_angle == pytest.approx(sign*.08)
+
+
+def test_driver_intervention_clears_feedback_through_existing_output_slew():
+  controller = ModelActionController()
+  for _ in range(100):
+    tick(controller, .004, .004)
+  for _ in range(100):
+    tick(controller, .004, .003)
+  assert controller.correction > 0.
+  previous = controller.c1
+  tick(controller, .004, -.01, feedback_enabled=False)
+  assert controller.correction == 0.
+  assert abs(controller.c1-previous) <= .0050000001
+  for _ in range(100):
+    out = tick(controller, .004, -.01, feedback_enabled=False)
+  assert controller.correction == 0.
+  assert out.path_angle == pytest.approx(.08)
+
+
+@pytest.mark.parametrize('field,value', [('current_curvature', math.nan), ('current_curvature', None),
+                                       ('current_curvature', 1.01), ('feedback_dt', math.nan),
+                                       ('feedback_dt', -.001), ('feedback_dt', .151), ('active', False)])
+def test_bad_feedback_inputs_and_disengagement_clear_every_control_state(field, value):
+  controller = ModelActionController()
+  controller.correction = .03
+  out = tick(controller, .004, .003, **{field: value})
+  assert out == FordPath()
+  assert (controller.c0, controller.c1, controller.correction) == (0., 0., 0.)
+
+
+def adapter_tick(controller, now, **overrides):
+  kwargs = {'current_curvature': .003, 'speed': 20., 'yaw_rate': 0., 'now': now,
+            'measurement_time': now, 'model_time': now, 'reference_time': now, 'active': True}
+  kwargs.update(overrides)
+  return controller.update(straight(.4), .004, **kwargs)
+
+
+def status(now, **overrides):
+  fields = {'valid': True, 'canMonoTime': round(now*1e9), 'limit': 0, 'lateralState': 2, 'denied': False}
+  fields.update(overrides)
+  return SimpleNamespace(**fields)
+
+
+def test_repeated_steering_samples_only_advance_output_slew():
+  controller = FordModelActionController()
+  for i in range(100):
+    adapter_tick(controller, 1.+i*.01, current_curvature=.004)
+  before = controller.core.correction
+  for i in range(1, 6):
+    adapter_tick(controller, 1.99+i*.01, measurement_time=1.99)
+    assert controller.core.correction == before
+  adapter_tick(controller, 2.05)
+  assert controller.core.correction == pytest.approx(.02*.06)
+  assert controller.diagnostics['feedback_dt'] == pytest.approx(.06)
+
+
+@pytest.mark.parametrize('overrides', [{'driver_pressed': True}, {'driver_torque': 1.01},
+                                     {'driver_torque': -1.01}, {'driver_torque': math.nan},
+                                     {'pscm_status': status(2.01, limit=3)},
+                                     {'pscm_status': status(2.01, denied=True)},
+                                     {'pscm_status': status(2.01, lateralState=1)}])
+def test_adapter_clears_feedback_when_driver_or_pscm_overrides(overrides):
+  controller = FordModelActionController()
+  for i in range(101):
+    adapter_tick(controller, 1.+i*.01)
+  assert controller.core.correction > 0.
+  assert adapter_tick(controller, 2.01, **overrides).valid
+  assert controller.core.correction == 0.
+  assert not controller.diagnostics['feedback_enabled']
+
+
+@pytest.mark.parametrize('overrides,limited', [({}, True), ({'valid': False}, False),
+                                            ({'canMonoTime': 0}, False), ({'canMonoTime': 1_800_000_000}, False),
+                                            ({'canMonoTime': 2_020_000_000}, False), ({'limit': 1}, False)])
+def test_only_fresh_reached_pscm_limit_blocks_outward_integration(overrides, limited):
+  controller = FordModelActionController()
+  for i in range(100):
+    adapter_tick(controller, 1.+i*.01, current_curvature=.004)
+  adapter_tick(controller, 2., pscm_status=status(2., **{'limit': 2, **overrides}))
+  assert controller.diagnostics['pscm_limited'] is limited
+  assert (controller.core.correction == 0.) is limited
+
+
+def test_measurement_cadence_preserves_elapsed_distance_integration():
+  results = []
+  for period in (1, 2, 5):
+    controller = FordModelActionController()
+    for i in range(101):
+      now = 1.+i*.01
+      adapter_tick(controller, now, current_curvature=.004)
+    for i in range(1, 101):
+      now = 2.+i*.01
+      adapter_tick(controller, now, measurement_time=2.+(i//period)*period*.01)
+    results.append(controller.core.correction)
+  assert results == pytest.approx([.02, .02, .02])
@@ -0,0 +1,100 @@
+"""Exercise real startup selection and Sunnylink writes without starting hardware."""
+import ast
+import base64
+import itertools
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from opendbc.car.ford.values import FordFlags
+from openpilot.common.params import Params, ParamKeyFlag, ParamKeyType
+from openpilot.selfdrive.controls.lib.ford_model_action import FordModelActionController, select_model_action_controller
+from openpilot.selfdrive.controls.lib.ford_path import FordPath, FordPathController, FordPscmObserverPathController
+
+
+def car_params(**overrides):
+  return SimpleNamespace(**({'brand': 'ford', 'flags': FordFlags.CANFD, 'carFingerprint': 'FORD_F_150_LIGHTNING_MK1',
+                             'carFw': []} | overrides))
+
+
+def startup(cp=None, params=None):
+  filename = Path(__file__).resolve().parents[1]/'controlsd.py'
+  tree = ast.parse(filename.read_text())
+  cls = next(n for n in tree.body if isinstance(n, ast.ClassDef) and n.name == 'Controls')
+  body = next(n for n in cls.body if isinstance(n, ast.FunctionDef) and n.name == '__init__').body
+  start = next(i for i, n in enumerate(body) if isinstance(n, ast.Assign) and ast.unparse(n.targets[0]) == 'self.ford_pscm_observer')
+  end = next(i for i, n in enumerate(body) if isinstance(n, ast.Assign) and ast.unparse(n.targets[0]) == 'self.ford_path')
+  if params is None:
+    params = SimpleNamespace(get_bool=lambda key: key == 'FordModelActionController')
+  controls = SimpleNamespace(CP=cp or car_params(), params=params)
+  environment = {'self': controls, 'FordFlags': FordFlags, 'FordPath': FordPath,
+                 'FordPathController': FordPathController, 'FordPscmObserverPathController': FordPscmObserverPathController,
+                 'FordModelActionController': FordModelActionController,
+                 'select_model_action_controller': select_model_action_controller,
+                 'cloudlog': SimpleNamespace(event=lambda *args, **kwargs: None)}
+  exec(compile(ast.Module(body=body[start:end+1], type_ignores=[]), str(filename), 'exec'), environment)
+  return controls
+
+
+@pytest.mark.parametrize('candidate,observer', list(itertools.product((False, True), repeat=2)))
+def test_actual_startup_priority(candidate, observer):
+  settings = {'FordModelActionController': candidate, 'FordPscmObserver': observer}
+  selected = startup(params=SimpleNamespace(get_bool=settings.__getitem__))
+  previous = FordPscmObserverPathController if observer else FordPathController
+  expected = FordModelActionController if candidate else previous
+  assert type(selected.ford_path_controller) is expected
+  assert selected.ford_model_action == candidate
+  assert selected.ford_path == FordPath()
+
+
+@pytest.mark.parametrize('overrides', [{'brand': 'tesla'}, {'flags': 0}, {'carFingerprint': 'FORD_F_150_MK14'}])
+@pytest.mark.parametrize('observer', [False, True])
+def test_other_vehicles_keep_their_previous_selection(overrides, observer):
+  settings = {'FordModelActionController': False, 'FordPscmObserver': observer}
+  params = SimpleNamespace(get_bool=settings.__getitem__)
+  before = startup(car_params(**overrides), params)
+  settings['FordModelActionController'] = True
+  after = startup(car_params(**overrides), params)
+  assert type(after.ford_path_controller) is type(before.ford_path_controller)
+  assert not after.ford_model_action
+
+
+@pytest.mark.parametrize('firmware', [[], [SimpleNamespace(ecu='eps', fwVersion=b'other')]])
+def test_candidate_does_not_depend_on_eps_firmware_query(firmware):
+  assert isinstance(startup(car_params(carFw=firmware)).ford_path_controller, FordModelActionController)
+
+
+@pytest.mark.parametrize('observer', [False, True])
+def test_sunnylink_write_takes_effect_on_restart_and_restores_stored_selection(tmp_path, monkeypatch, observer):
+  from openpilot.sunnypilot.sunnylink import utils
+
+  params = Params(str(tmp_path))
+  monkeypatch.setattr(utils, 'Params', lambda: params)
+  assert params.get_default_value('FordModelActionController') is False
+  assert params.get_type('FordModelActionController') == ParamKeyType.BOOL
+  assert b'FordModelActionController' in params.all_keys(ParamKeyFlag.PERSISTENT)
+  assert b'FordModelActionController' in params.all_keys(ParamKeyFlag.BACKUP)
+  params.put_bool('FordPscmObserver', observer, block=True)
+  old = startup(params=params)
+  assert not isinstance(old.ford_path_controller, FordModelActionController)
+  utils.save_param_from_base64_encoded_string('FordModelActionController', base64.b64encode(b'true').decode())
+  enabled = startup(params=params)
+  assert isinstance(enabled.ford_path_controller, FordModelActionController)
+  assert not isinstance(old.ford_path_controller, FordModelActionController)
+  utils.save_param_from_base64_encoded_string('FordModelActionController', base64.b64encode(b'false').decode())
+  assert isinstance(enabled.ford_path_controller, FordModelActionController)
+  assert type(startup(params=params).ford_path_controller) is type(old.ford_path_controller)
+  assert params.get_bool('FordPscmObserver') == observer
+
+
+def test_stored_retired_toggle_cannot_enable_the_candidate(tmp_path):
+  params = Params(str(tmp_path))
+  Path(params.get_param_path('FordVirtualAngleController')).write_text('1')
+  assert b'FordVirtualAngleController' not in params.all_keys()
+  assert params.get_bool('FordModelActionController') is False
+  assert type(startup(params=params).ford_path_controller) is FordPathController
+  params.put_bool('FordModelActionController', True, block=True)
+  params.clear_all(ParamKeyFlag.CLEAR_ON_MANAGER_START)
+  assert not Path(params.get_param_path('FordVirtualAngleController')).exists()
+  assert params.get_bool('FordModelActionController') is True
@@ -0,0 +1,422 @@
+import math
+from types import SimpleNamespace
+
+import numpy as np
+
+from openpilot.cereal import custom
+from openpilot.selfdrive.car.helpers import convert_carControlSP
+from openpilot.selfdrive.controls.lib.ford_path import (DBC_ANGLE, DBC_CURVATURE, DBC_OFFSET, FordPath, FordPathController,
+                                                        FordPscmObserver, FordPscmObserverPathController, FordPscmState,
+                                                        _bounded_feedback, _encode_path, _model_path, _predicted_pose,
+                                                        _pscm_contributions, _relative_pose)
+
+
+def _path(curvature: float, speed: float = 8.0):
+  t = np.linspace(0.0, 3.0, 61)
+  distance = speed * t
+  heading = curvature * distance
+  x = np.zeros_like(distance)
+  y = np.zeros_like(distance)
+  for i in range(1, len(distance)):
+    ds = distance[i] - distance[i - 1]
+    average_heading = 0.5 * (heading[i] + heading[i - 1])
+    x[i] = x[i - 1] + ds * math.cos(average_heading)
+    y[i] = y[i - 1] + ds * math.sin(average_heading)
+  return SimpleNamespace(
+    position=SimpleNamespace(t=t.tolist(), x=x.tolist(), y=y.tolist()),
+    orientation=SimpleNamespace(z=heading.tolist()),
+  )
+
+
+def _changing_path(start_curvature: float, end_curvature: float, speed: float = 8.0):
+  t = np.linspace(0.0, 3.0, 61)
+  distance = speed * t
+  curvature = np.interp(distance, [distance[0], min(distance[-1], 7.0)], [start_curvature, end_curvature])
+  heading = np.zeros_like(distance)
+  x = np.zeros_like(distance)
+  y = np.zeros_like(distance)
+  for i in range(1, len(distance)):
+    ds = distance[i] - distance[i - 1]
+    heading[i] = heading[i - 1] + 0.5 * (curvature[i] + curvature[i - 1]) * ds
+    average_heading = 0.5 * (heading[i] + heading[i - 1])
+    x[i] = x[i - 1] + ds * math.cos(average_heading)
+    y[i] = y[i - 1] + ds * math.sin(average_heading)
+  return SimpleNamespace(
+    position=SimpleNamespace(t=t.tolist(), x=x.tolist(), y=y.tolist()),
+    orientation=SimpleNamespace(z=heading.tolist()),
+  )
+
+
+def _command(model, desired_curvature: float, *, current_curvature: float = 0.0, v_ego: float = 8.0):
+  return FordPathController(dt=1.0).update(model, desired_curvature, current_curvature=current_curvature, v_ego=v_ego)
+
+
+def _equivalent_curvature(command) -> float:
+  return 2.0 * command.path_offset / 7.0 ** 2 + 2.0 * command.path_angle / 7.0 + command.curvature
+
+
+def test_gentle_path_uses_only_c2():
+  command = _command(_path(0.004, speed=20.0), 0.004, current_curvature=0.004, v_ego=20.0)
+  assert command.valid
+  assert command.path_offset == 0.0
+  assert command.path_angle == 0.0
+  assert np.isclose(command.curvature, 0.004, atol=1e-6)
+  assert command.curvature_rate == 0.0
+
+
+def test_gentle_path_uses_only_c2_when_model_and_action_disagree():
+  command = _command(_path(0.005), 0.002, current_curvature=0.005)
+  assert command.path_offset == 0.0
+  assert command.path_angle == 0.0
+  assert np.isclose(command.curvature, 0.002, atol=1e-6)
+
+
+def test_spatially_growing_path_adds_fast_pose_before_action_becomes_large():
+  controller = FordPathController(dt=1.0)
+  command = controller.update(_changing_path(0.0, 0.04), 0.012, current_curvature=0.0, v_ego=8.0)
+  assert command.path_offset > 0.0
+  assert command.path_angle > 0.0
+  assert command.curvature < 0.012
+  assert command.curvature_rate == 0.0
+
+
+def test_growing_model_pose_adds_authority_but_c3_is_never_transmitted():
+  constant = _command(_path(0.012), 0.012)
+  growing = _command(_changing_path(0.0, 0.04), 0.012)
+  assert _equivalent_curvature(growing) > _equivalent_curvature(constant)
+  assert constant.curvature_rate == 0.0
+  assert growing.curvature_rate == 0.0
+
+
+def test_local_tracking_error_corrects_without_replacing_forward_pose():
+  model = _changing_path(0.0, 0.04)
+  local_curvature = 0.5 * 0.04 * 2.0 / 7.0
+  aligned = _command(model, 0.012, current_curvature=local_curvature)
+  under = _command(model, 0.012, current_curvature=0.0)
+  assert aligned.path_offset > 0.0
+  assert aligned.path_angle > 0.0
+  assert under.path_offset > aligned.path_offset
+  assert under.path_angle > aligned.path_angle
+
+
+def test_large_maneuver_uses_fast_pose_and_zeros_c2():
+  command = _command(_path(0.04), 0.04)
+  assert command.path_offset > 0.5
+  assert command.path_angle > 0.2
+  assert command.curvature == 0.0
+  assert command.curvature_rate == 0.0
+
+
+def test_model_pose_can_trigger_maneuver_when_action_is_late():
+  command = _command(_path(0.04), 0.002)
+  assert command.path_offset > 0.5
+  assert command.path_angle > 0.2
+  assert command.curvature == 0.0
+
+
+def test_gentle_model_pose_does_not_replace_a_collapsed_action():
+  command = _command(_path(0.005), 0.0, current_curvature=0.005)
+  assert command.path_offset == 0.0
+  assert command.path_angle == 0.0
+  assert command.curvature == 0.0
+
+
+def test_changing_gentle_curve_keeps_upstream_strength_c2():
+  command = _command(_changing_path(0.0, 0.008), 0.004, current_curvature=0.0)
+  assert np.isclose(command.curvature, 0.004)
+  assert command.path_offset == 0.0
+  assert command.path_angle == 0.0
+
+
+def test_action_only_maneuver_cannot_invent_large_model_pose():
+  command = _command(_path(0.002), 0.04)
+  assert 0.0 < command.path_offset < 0.1
+  assert 0.0 < command.path_angle < 0.03
+  assert command.curvature == 0.0
+
+
+def test_nearby_demands_blend_continuously_without_a_mode_threshold():
+  low = _command(_path(0.0119), 0.0119)
+  high = _command(_path(0.0121), 0.0121)
+  assert abs(high.path_offset - low.path_offset) < 0.05
+  assert abs(high.path_angle - low.path_angle) < 0.03
+  assert abs(high.curvature - low.curvature) < 0.001
+
+
+def test_leaving_c2_normal_band_does_not_drop_total_authority():
+  normal = _command(_path(0.006), 0.006)
+  transition = _command(_path(0.0061), 0.0061)
+  assert transition.curvature <= normal.curvature
+  assert _equivalent_curvature(transition) >= _equivalent_curvature(normal)
+
+
+def test_low_speed_still_uses_available_model_pose():
+  command = _command(_path(0.04, speed=2.0), 0.04, v_ego=2.0)
+  assert command.path_offset > 0.0
+  assert command.path_angle > 0.0
+
+
+def test_higher_speed_advances_predicted_pose_and_extends_heading_horizon():
+  model = _changing_path(0.0, 0.015, speed=20.0)
+  slow = _command(model, 0.012, v_ego=7.0)
+  fast = _command(model, 0.012, v_ego=20.0)
+  assert fast.path_offset > slow.path_offset
+  assert fast.path_angle > slow.path_angle
+
+
+def test_short_model_uses_available_endpoint():
+  model = _path(0.04, speed=1.0)
+  command = _command(model, 0.04, v_ego=1.0)
+  assert command.valid
+  assert command.path_offset > 0.0
+  assert command.path_angle > 0.0
+
+
+def test_turn_entry_coordinates_c2_release_with_fast_pose_attack():
+  controller = FordPathController(dt=0.01)
+  for _ in range(20):
+    assert controller.update(_path(0.004), 0.004, v_ego=8.0).curvature > 0.0
+  outputs = [controller.update(_path(0.04), 0.04, current_curvature=0.01, v_ego=8.0) for _ in range(100)]
+  assert 0.0 < outputs[0].curvature < 0.004
+  assert outputs[0].path_offset > 0.0
+  assert outputs[0].path_angle > 0.0
+  assert outputs[-1].curvature == 0.0
+
+
+def test_turn_exit_allows_c2_to_take_over_while_fast_pose_drains():
+  controller = FordPathController(dt=0.01)
+  for _ in range(20):
+    controller.update(_path(0.04), 0.04, current_curvature=0.02, v_ego=8.0)
+  outputs = [controller.update(_path(0.004), 0.004, current_curvature=0.004, v_ego=8.0) for _ in range(100)]
+  assert 0.0 < outputs[0].curvature < 0.004
+  assert outputs[0].path_offset != 0.0 or outputs[0].path_angle != 0.0
+  assert outputs[-1].path_offset == 0.0
+  assert outputs[-1].path_angle == 0.0
+
+
+def test_100hz_handoff_preserves_total_authority_without_entry_drop_or_exit_overshoot():
+  controller = FordPathController(dt=0.01)
+  normal = controller.update(_path(0.006), 0.006, current_curvature=0.006, v_ego=8.0)
+  entries = [controller.update(_path(0.04), 0.04, current_curvature=0.01, v_ego=8.0) for _ in range(100)]
+  entry_authority = np.asarray([_equivalent_curvature(command) for command in entries])
+  assert np.all(np.diff(entry_authority) >= -1e-9)
+  assert entry_authority[0] >= _equivalent_curvature(normal)
+
+  exits = [controller.update(_path(0.004), 0.004, current_curvature=0.004, v_ego=8.0) for _ in range(100)]
+  exit_authority = np.asarray([_equivalent_curvature(command) for command in exits])
+  assert np.all(np.diff(exit_authority) <= 1e-9)
+  assert np.all(exit_authority >= 0.004 - 1e-9)
+
+
+def test_measured_tracking_error_closes_bidirectionally_without_abandoning_the_turn():
+  model = _path(0.04)
+  under = _command(model, 0.04, current_curvature=0.005)
+  on_target = _command(model, 0.04, current_curvature=0.04)
+  over = _command(model, 0.04, current_curvature=0.05)
+  assert under.path_offset > on_target.path_offset
+  assert under.path_angle > on_target.path_angle
+  assert 0.0 < over.path_offset < on_target.path_offset
+  assert 0.0 < over.path_angle < on_target.path_angle
+
+
+def test_gentle_curve_does_not_add_fast_tracking_trim():
+  model = _path(0.004)
+  under = _command(model, 0.004, current_curvature=0.002)
+  on_target = _command(model, 0.004, current_curvature=0.004)
+  over = _command(model, 0.004, current_curvature=0.006)
+  assert under.path_offset == on_target.path_offset == over.path_offset == 0.0
+  assert under.path_angle == on_target.path_angle == over.path_angle == 0.0
+  assert np.allclose([under.curvature, on_target.curvature, over.curvature], 0.004, atol=2e-6)
+
+
+def test_overshoot_trim_cannot_erase_a_modeled_turn():
+  model = _path(0.04)
+  on_target = _command(model, 0.04, current_curvature=0.04)
+  over = _command(model, 0.04, current_curvature=0.06)
+  assert over.path_offset > 0.95 * on_target.path_offset
+  assert over.path_angle > 0.9 * on_target.path_angle
+
+
+def test_corrupt_measured_curvature_cannot_reverse_a_modeled_turn():
+  command = _command(_path(0.04), 0.04, current_curvature=0.5)
+  assert command.path_offset > 0.0
+  assert command.path_angle > 0.0
+  assert command.curvature == 0.0
+
+
+def test_feedback_preserves_half_lsb_feedforward_direction():
+  for feedforward, resolution in ((0.006, 0.01), (0.0004, 0.0005)):
+    result = feedforward + _bounded_feedback(feedforward, -1.0, resolution, 1.0)
+    assert result >= 0.5 * resolution
+
+
+def test_recent_curvature_trend_advances_vehicle_pose_without_a_response_gain():
+  model = _model_path(_path(0.04))
+  assert model is not None
+  constant = _encode_path(model, 0.04, current_curvature=0.02, curvature_delta=0.0, v_ego=8.0)
+  rising = _encode_path(model, 0.04, current_curvature=0.02, curvature_delta=0.01, v_ego=8.0)
+  assert 0.0 < rising.path_offset < constant.path_offset
+  assert 0.0 < rising.path_angle < constant.path_angle
+
+
+def test_model_path_exit_zeros_lingering_c2_and_countersteers():
+  command = _command(_path(0.0), 0.004, current_curvature=0.006)
+  assert command.path_offset <= 0.0
+  assert command.path_angle < 0.0
+  assert command.curvature == 0.0
+
+
+def test_model_path_reversal_zeros_opposing_lingering_c2():
+  command = _command(_path(-0.004), 0.004, current_curvature=0.002)
+  assert command.path_offset < 0.0
+  assert command.path_angle < 0.0
+  assert command.curvature == 0.0
+
+
+def test_s_turn_reverses_model_pose_without_slow_c2():
+  controller = FordPathController(dt=0.05)
+  for _ in range(10):
+    controller.update(_path(0.04), 0.04, v_ego=8.0)
+  outputs = [controller.update(_path(-0.04), -0.04, v_ego=8.0) for _ in range(10)]
+  assert all(command.curvature == 0.0 for command in outputs)
+  assert np.all(np.diff([command.path_offset for command in outputs]) < 0.0)
+  assert np.all(np.diff([command.path_angle for command in outputs]) < 0.0)
+  assert outputs[-1].path_offset < 0.0
+  assert outputs[-1].path_angle < 0.0
+
+
+def test_output_limits_and_rates_are_bounded():
+  controller = FordPathController()
+  outputs = [controller.update(_path(0.2), 0.2, v_ego=8.0) for _ in range(100)]
+  assert all(DBC_OFFSET[0] <= command.path_offset <= DBC_OFFSET[1] for command in outputs)
+  assert all(DBC_ANGLE[0] <= command.path_angle <= DBC_ANGLE[1] for command in outputs)
+  assert all(DBC_CURVATURE[0] <= command.curvature <= DBC_CURVATURE[1] for command in outputs)
+  assert np.max(np.abs(np.diff([command.path_offset for command in outputs]))) <= 0.04 + 1e-9
+  assert np.max(np.abs(np.diff([command.path_angle for command in outputs]))) <= 0.01 + 1e-9
+
+
+def test_clipped_path_angle_uses_available_offset_to_preserve_endpoint():
+  horizon = 7.0
+  for curvature, angle_limit in ((-0.1, DBC_ANGLE[0]), (0.1, DBC_ANGLE[1])):
+    model = _path(curvature)
+    command = _command(model, curvature, current_curvature=curvature, v_ego=horizon)
+    path = _model_path(model)
+    assert path is not None
+    advance = 0.1 * horizon
+    model_offset, model_angle = _relative_pose(advance + horizon, path,
+                                                _predicted_pose(advance, curvature, 0.0))
+
+    assert command.path_angle == angle_limit
+    assert np.isclose(command.path_offset + horizon * command.path_angle,
+                      model_offset + horizon * model_angle)
+
+
+def test_invalid_model_ramps_pose_to_zero_and_inactive_resets():
+  controller = FordPathController(dt=0.01)
+  for _ in range(20):
+    active = controller.update(_path(0.04), 0.04, v_ego=8.0)
+  invalid = controller.update(None, 0.0, v_ego=8.0)
+  assert invalid.valid
+  assert abs(invalid.path_offset) < abs(active.path_offset)
+  assert abs(invalid.path_angle) < abs(active.path_angle)
+  assert not controller.update(_path(0.0), 0.0, v_ego=8.0, active=False).valid
+
+
+def test_sunnypilot_path_message_round_trip():
+  message = custom.CarControlSP.new_message()
+  message.fordLateralPath.pathOffset = 0.3
+  message.fordLateralPath.pathAngle = -0.2
+  message.fordLateralPath.curvature = 0.008
+  message.fordLateralPath.curvatureRate = -0.0004
+  message.fordLateralPath.valid = True
+  path = convert_carControlSP(message.as_reader()).fordLateralPath
+  assert np.isclose(path.pathOffset, 0.3)
+  assert np.isclose(path.pathAngle, -0.2)
+  assert np.isclose(path.curvature, 0.008)
+  assert np.isclose(path.curvatureRate, -0.0004)
+  assert path.valid
+
+
+def test_pscm_observer_mirrors_exact_250hz_slew_and_c3_target():
+  observer = FordPscmObserver()
+  observer.set_command(FordPath(True, 1.0, 0.5, 0.0, 0.001))
+  observer.advance(1.0)
+  assert np.isclose(observer.state.path_offset, 1.0)
+  assert np.isclose(observer.state.path_angle, 0.100006103515625)
+  assert np.isclose(observer.state.curvature, 0.0030059814453125)
+
+
+def test_pscm_observer_tracks_wire_quantized_commands():
+  observer = FordPscmObserver()
+  observer.set_command(FordPath(True, 0.006, 0.0004, 0.000011, 0.0))
+  assert observer.command.path_offset == 0.01
+  assert observer.command.path_angle == 0.0005
+  assert observer.command.curvature == 0.00002
+
+
+def test_pscm_c2_contribution_is_speed_scheduled():
+  state = FordPscmObserver().state
+  state = type(state)(curvature=0.004)
+  low = _pscm_contributions(state, 5.0)[2]
+  high = _pscm_contributions(state, 20.0)[2]
+  assert high > low * 10.0
+
+
+def test_pscm_observer_fills_missing_gentle_c2_with_fast_fields():
+  controller = FordPscmObserverPathController(dt=0.01)
+  command = controller.update(_path(0.004, speed=20.0), 0.004, current_curvature=0.004,
+                              v_ego=20.0, v_ego_raw=20.0)
+  assert command.path_offset > 0.0
+  assert command.path_angle > 0.0
+  assert command.curvature > 0.0
+
+
+def test_pscm_observer_uses_c0_only_after_c1_reaches_its_effective_limit():
+  controller = FordPscmObserverPathController(dt=0.01)
+  small = controller._command_for_state(FordPath(True, 0.2, 0.0, 0.0, 0.0), 8.0)
+  large = controller._command_for_state(FordPath(True, 1.0, 0.5, 0.0, 0.0), 8.0)
+  assert small.path_offset == 0.0
+  assert small.path_angle > 0.0
+  assert large.path_offset > 0.0
+  assert large.path_angle == 0.349609375 / 10.0
+
+
+def test_pscm_observer_preserves_c2_residual_across_c0_c1_headroom():
+  controller = FordPscmObserverPathController(dt=0.01)
+  target = FordPath(True, 0.0, 0.0, 0.004, 0.0)
+  command = controller._command_for_state(target, 20.0)
+  target_contribution = sum(_pscm_contributions(FordPscmState(curvature=target.curvature), 20.0))
+  command_contributions = _pscm_contributions(FordPscmState(command.path_offset, command.path_angle), 20.0)
+  assert np.isclose(sum(command_contributions), target_contribution)
+
+  controller.observer.state = FordPscmState(curvature=0.004)
+  unwind = controller._command_for_state(FordPath(valid=True), 20.0)
+  unwind_contributions = _pscm_contributions(FordPscmState(unwind.path_offset, unwind.path_angle), 20.0)
+  lingering_c2 = _pscm_contributions(controller.observer.state, 20.0)[2]
+  assert np.isclose(sum(unwind_contributions) + lingering_c2, 0.0)
+
+
+def test_pscm_observer_unloads_fast_residual_as_c2_loads():
+  controller = FordPscmObserverPathController(dt=0.01)
+  outputs = [controller.update(_path(0.004, speed=20.0), 0.004, current_curvature=0.004,
+                               v_ego=20.0, v_ego_raw=20.0) for _ in range(200)]
+  assert outputs[0].path_angle > outputs[-1].path_angle >= 0.0
+  assert controller.observer.state.curvature > 0.003
+
+
+def test_pscm_observer_counters_lingering_c2_during_model_exit():
+  controller = FordPscmObserverPathController(dt=0.01)
+  for _ in range(200):
+    controller.update(_path(0.004, speed=20.0), 0.004, current_curvature=0.004,
+                      v_ego=20.0, v_ego_raw=20.0)
+  command = controller.update(_path(0.0, speed=20.0), 0.0, current_curvature=0.004,
+                              v_ego=20.0, v_ego_raw=20.0)
+  assert command.path_angle < 0.0
+  assert command.curvature < controller.observer.state.curvature
+
+
+def test_pscm_observer_avoids_ineffective_c0_c1_windup():
+  controller = FordPscmObserverPathController(dt=1.0)
+  command = controller.update(_path(0.2), 0.2, v_ego=8.0, v_ego_raw=8.0)
+  assert abs(command.path_offset) <= 1.0
+  assert abs(command.path_angle) <= 0.349609375 / 10.0
@@ -7,14 +7,9 @@ from openpilot.common.file_chunker import chunk_file, get_chunk_targets, get_exi
 from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
 from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE, DM_INPUT_SIZE
 from openpilot.selfdrive.modeld.constants import ModelConstants
-from openpilot.selfdrive.modeld.helpers import TG_INPUT_DEVICES_PATH, usbgpu_present, modeld_pkl_path
+from openpilot.selfdrive.modeld.helpers import TG_INPUT_DEVICES_PATH, chestnut_present, modeld_pkl_path


-CAMERA_CONFIGS = [
-  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),  # tici: 1928x1208
-  (_os_fisheye.width, _os_fisheye.height),        # mici: 1344x760
-]
-
 Import('env', 'arch')
 chunker_file = File("#openpilot/common/file_chunker.py")
 lenv = env.Clone()
@@ -24,30 +19,32 @@ tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "
                  if 'pycache' not in x and os.path.isfile(os.path.join(tinygrad_root, x))]

 def estimate_pickle_max_size(onnx_size):
-  return 1.2 * onnx_size  + 10 * 1024 * 1024  # 20% + 10MB is plenty
+  # QCOM programs for models with spatial recurrent features can approach 2x
+  # the ONNX size. Overestimating only adds an empty trailing chunk.
+  return 2.0 * onnx_size + 10 * 1024 * 1024

 if arch == 'comma_arm64':
+  from openpilot.common.hardware import HARDWARE
+  camera = _os_fisheye if HARDWARE.get_device_type() == "mici" else _ar_ox_fisheye
+  camera_configs = [(camera.width, camera.height)]
  tg_backend = 'QCOM'
  tg_flags = f'DEV={tg_backend} IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1'
 else:
+  camera_configs = [(c.width, c.height) for c in (_ar_ox_fisheye, _os_fisheye)]
  tg_backend = 'CPU'
  tg_flags = f'DEV=CPU' if arch == 'Darwin' else 'DEV=CPU:LLVM'

 tg_devices = { # which device to put jit inputs to at runtime
-  'openpilot.selfdrive.modeld.modeld': {
-    'default': {'WARP_DEV': tg_backend, 'QUEUE_DEV': tg_backend},
-    'usbgpu': {'WARP_DEV': tg_backend, 'QUEUE_DEV': 'AMD'}
-  },
  'openpilot.selfdrive.modeld.dmonitoringmodeld': {
    'default': {'DEV': tg_backend}
  },
 }

-USBGPU = usbgpu_present()
-if USBGPU:
-  usbgpu_tg_flags = f'DEBUG=2 DEV=USB+AMD:LLVM WARP_DEV={tg_backend} FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2'
+CHESTNUT = chestnut_present()
+if CHESTNUT:
+  chestnut_tg_flags = 'DEBUG=1 DEV=USB+AMD:LLVM FRAME_DEV=CPU FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_OCCUPANCY_OPT=1'
  # the USB+AMD GPU takes an exclusive flock; serialize all targets that touch it
-  usbgpu_lock = File("models/.usb_gpu.lock").abspath
+  chestnut_lock = File("models/.chestnut.lock").abspath

 def write_tg_devices(target, source, env):
  with open(str(target[0]), "w") as f:
@@ -73,44 +70,44 @@ compile_modeld_script = [
 model_w, model_h = MEDMODEL_INPUT_SIZE
 frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ

-for usbgpu in [False, True] if USBGPU else [False]:
-  target_pkl_path = File(modeld_pkl_path(usbgpu)).abspath
-  # BIG_INTO_SMALL=1 builds the default target from the big model, e.g. to test it without a USB GPU
-  file_prefix, cmd_flags = ('big_', usbgpu_tg_flags) if usbgpu else ('big_' if os.getenv('BIG_INTO_SMALL') else '', tg_flags)
-  driving_onnx_deps = get_existing_chunks(File(f"models/{file_prefix}driving_supercombo.onnx").abspath)
-  camera_res_args = ' '.join(f'{cw}x{ch}' for cw, ch in CAMERA_CONFIGS)
-  # CPU 7 is isolated with isolcpus on AGNOS, so explicitly pin the compiler to it.
-  taskset = 'taskset -c 7 ' if arch == 'comma_arm64' else ''
-  cmd = (f'{cmd_flags} {mac_brew_string} {taskset}python3 {modeld_dir}/compile_modeld.py '
-        f'--model-size {model_w}x{model_h} '
-        f'--camera-resolutions {camera_res_args} '
-        f'--onnx {File(f"models/{file_prefix}driving_supercombo.onnx").abspath} '
-        f'--output {target_pkl_path} --frame-skip {frame_skip}')
-  onnx_sizes_sum = sum(os.path.getsize(f) for f in driving_onnx_deps)
-  chunk_targets = get_chunk_targets(target_pkl_path, estimate_pickle_max_size(onnx_sizes_sum))
-  def do_compile(target, source, env, command=cmd, pkl=target_pkl_path, chunks=chunk_targets):
-    from openpilot.system.hardware.chestnut.flash import link_up
-    # chestnut can enumerate before its PCIe link is up due to varying 12V power behavior across cars
-    for _ in range(10):
-      if link_up():
-        break
-      time.sleep(1)
-    else:
-      print("Chestnut not ready, skipping big model build")
-      return
-    if ret := env.Execute(command):
-      return ret
-    chunk_file(pkl, chunks)
-  def do_chunk(target, source, env, pkl=target_pkl_path, chunks=chunk_targets):
-    chunk_file(pkl, chunks)
-  actions = Action(do_compile, "  [USBGPU] $TARGET") if usbgpu else [cmd, Action(do_chunk, "  [CHUNK] $TARGET")]
-  node = lenv.Command(
-    chunk_targets,
-    tinygrad_files + compile_modeld_script + driving_onnx_deps + [Value(chunk_targets), chunker_file],
-    actions,
-  )
-  if usbgpu:
-    lenv.SideEffect(usbgpu_lock, node)
+if not os.getenv('SKIP_TINYGRAD_COMPILE'):
+  for chestnut in [False, True] if CHESTNUT else [False]:
+    target_pkl_path = File(modeld_pkl_path(chestnut)).abspath
+    file_prefix, cmd_flags = ('big_', chestnut_tg_flags) if chestnut else ('', tg_flags)
+    driving_onnx_deps = get_existing_chunks(File(f"models/{file_prefix}driving_supercombo.onnx").abspath)
+    camera_res_args = ' '.join(f'{cw}x{ch}' for cw, ch in camera_configs)
+    # CPU 7 is isolated with isolcpus on AGNOS, so explicitly pin the compiler to it.
+    taskset = 'taskset -c 7 ' if arch == 'comma_arm64' else ''
+    cmd = (f'{cmd_flags} {mac_brew_string} {taskset}python3 {modeld_dir}/compile_modeld.py '
+          f'--model-size {model_w}x{model_h} '
+          f'--camera-resolutions {camera_res_args} '
+          f'--onnx {File(f"models/{file_prefix}driving_supercombo.onnx").abspath} '
+          f'--output {target_pkl_path} --frame-skip {frame_skip}')
+    onnx_sizes_sum = sum(os.path.getsize(f) for f in driving_onnx_deps)
+    chunk_targets = get_chunk_targets(target_pkl_path, estimate_pickle_max_size(onnx_sizes_sum))
+    def do_compile(target, source, env, command=cmd, pkl=target_pkl_path, chunks=chunk_targets):
+      from openpilot.system.hardware.chestnut.flash import link_up
+      # chestnut can enumerate before its PCIe link is up due to varying 12V power behavior across cars
+      for _ in range(10):
+        if link_up():
+          break
+        time.sleep(1)
+      else:
+        print("Chestnut not ready, skipping big model build")
+        return
+      if ret := env.Execute(command):
+        return ret
+      chunk_file(pkl, chunks)
+    def do_chunk(target, source, env, pkl=target_pkl_path, chunks=chunk_targets):
+      chunk_file(pkl, chunks)
+    actions = Action(do_compile, "  [CHESTNUT] $TARGET") if chestnut else [cmd, Action(do_chunk, "  [CHUNK] $TARGET")]
+    node = lenv.Command(
+      chunk_targets,
+      tinygrad_files + compile_modeld_script + driving_onnx_deps + [Value(camera_res_args), Value(chunk_targets), chunker_file],
+      actions,
+    )
+    if chestnut:
+      lenv.SideEffect(chestnut_lock, node)

 # get model metadata
 fn = File(f"models/dmonitoring_model").abspath
@@ -120,7 +117,7 @@ lenv.Command(fn + "_metadata.pkl", [fn + ".onnx"] + tinygrad_files + script_file

 dm_w, dm_h = DM_INPUT_SIZE
 compile_dm_warp_script = [File(f"{modeld_dir}/compile_dm_warp.py")]
-for cam_w, cam_h in CAMERA_CONFIGS:
+for cam_w, cam_h in camera_configs:
  dm_pkl_path = File(f"models/dm_warp_{cam_w}x{cam_h}_tinygrad.pkl").abspath
  cmd = (f'{tg_flags} {mac_brew_string} python3 {modeld_dir}/compile_dm_warp.py '
         f'--camera-resolution {cam_w}x{cam_h} --warp-to {dm_w}x{dm_h} '
@@ -37,17 +37,12 @@ from tinygrad.engine.jit import TinyJit


 NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
-WARP_INPUTS = ['tfm', 'big_tfm']
-POLICY_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
-
-UV_SCALE_MATRIX = np.array([[0.5, 0, 0], [0, 0.5, 0], [0, 0, 1]], dtype=np.float32)
-UV_SCALE_MATRIX_INV = np.linalg.inv(UV_SCALE_MATRIX)
-
-WARP_DEV = os.getenv('WARP_DEV')
+MODELD_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']


-def make_random_images(keys, shape, device=None):
-  return {k: Tensor.randint(shape, low=0, high=256, dtype='uint8', device=device).realize() for k in keys}
+def nv12_copy_size(stride: int, y_height: int, uv_height: int) -> int:
+  # Retain the padded Y and UV plane storage, but skip the trailing kernel/guard allocation.
+  return stride * (y_height + uv_height)


 def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad, border_fill_val=None):
@@ -99,7 +94,7 @@ def make_frame_prepare(nv12: NV12Frame, model_w, model_h):

  def frame_prepare_tinygrad(input_frame, M_inv):
    # UV_SCALE @ M_inv @ UV_SCALE_INV simplifies to elementwise scaling
-    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]], device=WARP_DEV)
+    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]], device=Device.DEFAULT)
    # deinterleave NV12 UV plane (UVUV... -> separate U, V)
    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
    with Context(SPLIT_REDUCEOP=0):
@@ -118,49 +113,43 @@ def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
  return frame_prepare_tinygrad


-def make_warp_input_queues(vision_input_shapes, frame_skip, device):
-  img = vision_input_shapes['img']  # (1, 12, 128, 256)
-  n_frames = img[1] // 6
-  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
-
-  npy = {
-    'tfm': np.zeros((3, 3), dtype=np.float32),
-    'big_tfm': np.zeros((3, 3), dtype=np.float32),
-  }
-  input_queues = {
-    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
-  }
-  return input_queues, npy
-
-
 def get_policy_npy_shapes(input_shapes):
  dp = input_shapes['desire_pulse']  # (1, 25, 8)
  tc = input_shapes['traffic_convention']  # (1, 2)
  at = input_shapes['action_t']  # (1, 2)
-  fb = input_shapes['features_buffer']  # (1, 24, 512)
+  fb = input_shapes['features_buffer']  # (1, T-1, ...) e.g. (1, 24, 32, 512) with spatial features
+  feat_dim = math.prod(fb[2:])
  # TODO prev_feat shouldn't exist and be handled inside the JIT, but corrupt on QCOM for now
-  shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], fb[2])}
+  shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], feat_dim)}
  return shapes, [math.prod(s) for s in shapes.values()]


-def make_input_queues(input_shapes, frame_skip, device):
-  input_queues, npy = make_warp_input_queues(input_shapes, frame_skip, device)
-
-  fb = input_shapes['features_buffer']  # (1, 24, 512), past features only; the model appends the current frame's feature
+def make_input_queues(input_shapes, frame_skip, device, frame_copy_size):
+  img = input_shapes['img']  # (1, 12, 128, 256)
+  fb = input_shapes['features_buffer']  # (1, T-1, ...), past features only; the model appends the current frame's feature
+  feat_dim = math.prod(fb[2:])
  dp = input_shapes['desire_pulse']  # (1, 25, 8)
+  n_frames = img[1] // 6
+  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])

-  shapes, sizes = get_policy_npy_shapes(input_shapes)
-  packed_npy_inputs = np.zeros(sum(sizes), dtype=np.float32)
+  policy_shapes, _ = get_policy_npy_shapes(input_shapes)
+  shapes = {'tfm': (3, 3), 'big_tfm': (3, 3)} | policy_shapes
+  sizes = [math.prod(s) for s in shapes.values()]
+  packed_npy_size = sum(sizes) * np.dtype(np.float32).itemsize
+  packed_input = np.zeros(packed_npy_size + 2 * frame_copy_size, dtype=np.uint8)
+  packed_npy_inputs = packed_input[:packed_npy_size].view(np.float32)
+  frames = packed_input[packed_npy_size:]
+  frame_views = {'img': frames[:frame_copy_size], 'big_img': frames[frame_copy_size:]}
  # views into the packed inputs, to be refilled at runtime
-  npy.update({k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)})
-  input_queues.update({
-    'feat_q': Tensor(np.zeros((frame_skip * fb[1], fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),
+  npy = {k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)}
+  input_queues = {
+    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'feat_q': Tensor(np.zeros((frame_skip * fb[1], fb[0], feat_dim), dtype=np.float32), device=device).contiguous().realize(),
    'desire_q': Tensor(np.zeros((frame_skip * dp[1], dp[0], dp[2]), dtype=np.float32), device=device).contiguous().realize(),
-    'packed_npy_inputs': Tensor(packed_npy_inputs, device='NPY').realize(),
-  })
-  return input_queues, npy
+    'packed_npy_inputs': Tensor(packed_input, device='NPY').realize(),
+  }
+  return input_queues, npy, frame_views


 def shift_and_sample(buf, new_val, sample_fn):
@@ -176,13 +165,15 @@ def sample_desire(buf, frame_skip):
  return buf.reshape(-1, frame_skip, *buf.shape[1:]).max(1).flatten(0, 1).unsqueeze(0)


-def make_warp(nv12, model_w, model_h, frame_skip):
+def make_warp(nv12, model_w, model_h):
  frame_prepare = make_frame_prepare(nv12, model_w, model_h)

  def warp(tfm, big_tfm, frame, big_frame):
-    tfm = tfm.to(WARP_DEV)
-    big_tfm = big_tfm.to(WARP_DEV)
-    Tensor.realize(tfm, big_tfm)
+    tfm = tfm.to(Device.DEFAULT)
+    big_tfm = big_tfm.to(Device.DEFAULT)
+    frame = frame.to(Device.DEFAULT)
+    big_frame = big_frame.to(Device.DEFAULT)
+    Tensor.realize(tfm, big_tfm, frame, big_frame)

    warped_frame = frame_prepare(frame, tfm).unsqueeze(0)
    warped_big_frame = frame_prepare(big_frame, big_tfm).unsqueeze(0)
@@ -195,10 +186,10 @@ def make_run_policy(model_runner, model_metadata, frame_skip):
  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
  npy_shapes, npy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
+  model_input_dtypes = {name: spec.dtype for name, spec in model_runner.graph_inputs.items()}

  def run_policy(warped, img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
    packed_npy_inputs = packed_npy_inputs.to(Device.DEFAULT)
-    warped = warped.to(Device.DEFAULT)
    Tensor.realize(packed_npy_inputs, warped)

    img = shift_and_sample(img_q, warped[0:1], sample_skip_fn)
@@ -211,33 +202,50 @@ def make_run_policy(model_runner, model_metadata, frame_skip):
    inputs = {
      'img': img,
      'big_img': big_img,
-      'features_buffer': feat_buf,
+      'features_buffer': feat_buf.reshape(model_metadata['input_shapes']['features_buffer']),
      'desire_pulse': desire_buf,
      'traffic_convention': traffic_convention,
      'action_t': action_t,
    }
+    inputs = {name: value.cast(model_input_dtypes[name]) for name, value in inputs.items()}
    out = next(iter(model_runner(inputs).values())).cast('float32')
    return out,
  return run_policy


-def compile_jit(jit, make_random_inputs, input_keys, make_queues):
-  SEED = 42
-  def random_inputs_run(fn, seed, test_val=None, test_buffers=None, expect_match=True):
-    input_queues, npy = make_queues(Device.DEFAULT)
-    rng = np.random.default_rng(seed)
-    Tensor.manual_seed(seed)
+def make_run_model(warp, run_policy, model_metadata, frame_copy_size):
+  _, policy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
+  packed_npy_size = (18 + sum(policy_sizes)) * np.dtype(np.float32).itemsize

-    testing = test_val is not None or test_buffers is not None
-    n_runs = 1 if testing else 3
+  def run_model(img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
+    packed_input = packed_npy_inputs.to(Device.DEFAULT)
+    Tensor.realize(packed_input)
+    packed_npy_inputs = packed_input[:packed_npy_size].bitcast('float32')
+    frame = packed_input[packed_npy_size:packed_npy_size + frame_copy_size]
+    big_frame = packed_input[packed_npy_size + frame_copy_size:]
+    tfm, big_tfm, policy_inputs = packed_npy_inputs.split([9, 9, sum(policy_sizes)])
+    warped = warp(tfm.reshape(3, 3), big_tfm.reshape(3, 3), frame, big_frame)
+    return run_policy(warped, img_q, big_img_q, feat_q, desire_q, policy_inputs)
+  return run_model
+
+
+def compile_jit(jit, input_keys, make_queues, benchmark_runs):
+  if benchmark_runs < 1:
+    raise ValueError("benchmark_runs must be at least 1")
+
+  SEED = 42
+  def random_inputs_run(fn, seed, n_runs, test_val=None, test_buffers=None, expect_match=True):
+    input_queues, npy, frame_views = make_queues(Device.DEFAULT)
+    rng = np.random.default_rng(seed)

    for i in range(n_runs):
      for v in npy.values():
        v[:] = rng.standard_normal(v.shape).astype(v.dtype)
+      for v in frame_views.values():
+        v[:] = rng.integers(0, 256, size=v.shape, dtype=np.uint8)
      Device.default.synchronize()
-      random_inputs = make_random_inputs()
      st = time.perf_counter()
-      outs = fn(**{k: input_queues[k] for k in input_keys}, **random_inputs)
+      outs = fn(**{k: input_queues[k] for k in input_keys})
      mt = time.perf_counter()
      Device.default.synchronize()
      et = time.perf_counter()
@@ -256,14 +264,15 @@ def compile_jit(jit, make_random_inputs, input_keys, make_queues):
    return val, buffers

  print('capture + replay')
-  test_val, test_buffers = random_inputs_run(jit, SEED)
-  print('pickle round trip')
+  test_val, test_buffers = random_inputs_run(jit, SEED, 3)
+  print(f'pickle round trip ({benchmark_runs} runs per seed)')
  with tempfile.TemporaryFile(dir=".") as f:
    dump_oob(jit, f)
    f.seek(0)
-    jit = load_oob(f)
-  random_inputs_run(jit, SEED, test_val, test_buffers, expect_match=True)
-  random_inputs_run(jit, SEED+1, test_val, test_buffers, expect_match=False)
+    loaded_jit = load_oob(f)
+  random_inputs_run(loaded_jit, SEED, benchmark_runs, test_val, test_buffers, expect_match=True)
+  random_inputs_run(loaded_jit, SEED+1, benchmark_runs, test_val, test_buffers, expect_match=False)
+  # Keep the original so per-resolution JITs share model weight buffers in the final pickle.
  return jit


@@ -292,27 +301,31 @@ if __name__ == "__main__":
  p.add_argument('--onnx', required=True)
  p.add_argument('--output', required=True)
  p.add_argument('--frame-skip', type=int, required=True)
+  p.add_argument('--benchmark-runs', type=int, default=1,
+                 help='timed loaded-JIT runs for each correctness seed')
  args = p.parse_args()

  model_path = read_file_chunked_to_disk(args.onnx)
  model_w, model_h = args.model_size

  model_runner = OnnxRunner(model_path)
-  out = {'metadata': make_metadata_dict(model_path)}
+  out = {
+    'metadata': make_metadata_dict(model_path),
+    'input_devices': {'model': Device.DEFAULT},
+    'run_model': {},
+  }

-  run_policy_jit = TinyJit(make_run_policy(model_runner, out['metadata'], args.frame_skip), prune=True)
-
-  make_policy_queues = partial(make_input_queues, out['metadata']['input_shapes'], args.frame_skip)
-  make_random_model_inputs = partial(make_random_images, keys=['warped'], shape=(2, 6, *out['metadata']['input_shapes']['img'][2:]), device=WARP_DEV)
-  out['run_policy'] = compile_jit(run_policy_jit, make_random_model_inputs, POLICY_INPUTS,
-                                  make_policy_queues)
+  run_policy = make_run_policy(model_runner, out['metadata'], args.frame_skip)

  for cam_w, cam_h in args.camera_resolutions:
    nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-    make_random_warp_inputs = partial(make_random_images, keys=['frame', 'big_frame'], shape=nv12.size, device=WARP_DEV)
-    warp = TinyJit(make_warp(nv12, model_w, model_h, args.frame_skip), prune=True)
-    make_warp_queues = partial(make_warp_input_queues, out['metadata']['input_shapes'], args.frame_skip)
-    out[(cam_w,cam_h)] = compile_jit(warp, make_random_warp_inputs, WARP_INPUTS, make_warp_queues)
+    frame_copy_size = nv12_copy_size(nv12.stride, nv12.y_height, nv12.uv_height)
+    make_model_queues = partial(make_input_queues, out['metadata']['input_shapes'], args.frame_skip,
+                                frame_copy_size=frame_copy_size)
+    warp = make_warp(nv12, model_w, model_h)
+    run_model_jit = TinyJit(make_run_model(warp, run_policy, out['metadata'], frame_copy_size), prune=True)
+    out['run_model'][(cam_w,cam_h)] = compile_jit(run_model_jit, MODELD_INPUTS, make_model_queues,
+                                                  args.benchmark_runs)

  with open(args.output, "wb") as f:
    dump_oob(out, f)
@@ -29,7 +29,7 @@ class ModelState:
  output: np.ndarray

  def __init__(self, cam_w: int, cam_h: int):
-    self.DEV = get_tg_input_devices(PROCESS_NAME, usbgpu=False)['DEV']
+    self.DEV = get_tg_input_devices(PROCESS_NAME, chestnut=False)['DEV']
    with open(METADATA_PATH, 'rb') as f:
      model_metadata = pickle.load(f)
      self.input_shapes = model_metadata['input_shapes']
@@ -64,6 +64,7 @@ def fill_driving_model_data(msg: capnp._DynamicStructBuilder, modelv2_send: capn
  driving_model_data.frameIdExtra = modelV2.frameIdExtra
  driving_model_data.frameDropPerc = modelV2.frameDropPerc
  driving_model_data.modelExecutionTime = modelV2.modelExecutionTime
+  driving_model_data.big = modelV2.big
  driving_model_data.action = modelV2.action
  driving_model_data.meta.laneChangeState = modelV2.meta.laneChangeState
  driving_model_data.meta.laneChangeDirection = modelV2.meta.laneChangeDirection
@@ -7,18 +7,20 @@ import tempfile
 from pathlib import Path

 from openpilot.common.file_chunker import get_manifest_path
-from openpilot.common.hardware.usb import CHESTNUT_FW_VERSION, CHESTNUT_USB_IDS, USB_DEVICES_PATH
+from openpilot.common.hardware.usb import CHESTNUT_USB_PRODUCT, USB_DEVICES_PATH, is_chestnut_usb_id

 MODELS_DIR = Path(__file__).resolve().parent / 'models'
 TG_INPUT_DEVICES_PATH = MODELS_DIR / 'tg_input_devices.json'
+CHESTNUT_POWERED_VOLTAGE = 5000
+CHESTNUT_PCIE_READY = 0x78


-def get_tg_input_devices(process_name: str, usbgpu: bool):
+def get_tg_input_devices(process_name: str, chestnut: bool):
  with open(TG_INPUT_DEVICES_PATH) as f:
-    return json.load(f)[process_name]['default' if not usbgpu else 'usbgpu']
+    return json.load(f)[process_name]['default' if not chestnut else 'chestnut']

-def modeld_pkl_path(usbgpu: bool):
-  prefix = 'big_' if usbgpu else ''
+def modeld_pkl_path(chestnut: bool):
+  prefix = 'big_' if chestnut else ''
  return MODELS_DIR / f'{prefix}driving_tinygrad.pkl'

 def dump_oob(obj, f):
@@ -45,16 +47,20 @@ def load_oob(f):
      yield pb
  return pickle.load(io.BytesIO(opcodes), buffers=buffers())

-def usbgpu_present() -> bool:
+def chestnut_present() -> bool:
  for d in USB_DEVICES_PATH.glob("*"):
    try:
      usb_id = (int((d / "idVendor").read_text(), 16), int((d / "idProduct").read_text(), 16))
      product = (d / "product").read_text().strip()
-      if usb_id in CHESTNUT_USB_IDS and product == f"custom {CHESTNUT_FW_VERSION}-CLEAN":
+      if is_chestnut_usb_id(*usb_id) and product == CHESTNUT_USB_PRODUCT:
        return True
    except Exception:
      pass
  return False

-def usbgpu_compiled() -> bool:
-  return Path(get_manifest_path(modeld_pkl_path(usbgpu=True))).is_file()
+def chestnut_compiled() -> bool:
+  return Path(get_manifest_path(modeld_pkl_path(chestnut=True))).is_file()
+
+
+def chestnut_ready(state) -> bool:
+  return state.supplyVoltage >= CHESTNUT_POWERED_VOLTAGE and not state.supplyFault and state.pcieLtssm == CHESTNUT_PCIE_READY
@@ -1,9 +1,11 @@
 #!/usr/bin/env python3
+from collections.abc import Callable
+import ctypes
 from functools import cached_property
 import os
-os.environ['GMMU'] = '0' # for usbgpu fast loading, noop for qcom
-from tinygrad.tensor import Tensor
+os.environ['GMMU'] = '0' # for chestnut fast loading, noop for qcom
 from tinygrad.device import Device
+import usb1
 import struct
 import threading
 import time
@@ -26,17 +28,17 @@ from openpilot.common.transformations.model import get_warp_matrix
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
 from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan, should_stop, smooth_value, get_curvature_from_plan
 from openpilot.selfdrive.modeld.parse_model_outputs import Parser
-from openpilot.selfdrive.modeld.compile_modeld import make_input_queues, WARP_INPUTS, POLICY_INPUTS
+from openpilot.selfdrive.modeld.compile_modeld import make_input_queues, nv12_copy_size, MODELD_INPUTS
 from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_driving_model_data, fill_pose_msg, PublishState
 from openpilot.common.file_chunker import open_file_chunked
+from openpilot.common.hardware.usb import CHESTNUT_USB_IDS
 from openpilot.selfdrive.modeld.constants import ModelConstants, Plan
-from openpilot.selfdrive.modeld.helpers import usbgpu_present, usbgpu_compiled, modeld_pkl_path, get_tg_input_devices, load_oob
+from openpilot.selfdrive.modeld.helpers import chestnut_present, chestnut_compiled, chestnut_ready, modeld_pkl_path, load_oob

 from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
 from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
 from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController

-PROCESS_NAME = "openpilot.selfdrive.modeld.modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')

 LAT_SMOOTH_SECONDS = 0.0
@@ -81,6 +83,37 @@ class ChestnutState:
    self.valid = True
    self.sends = 0
    self.metrics = {}
+    self._asm_usb = None
+
+  def _close_asm_usb(self) -> None:
+    if self._asm_usb is not None:
+      self._asm_usb.close()
+      self._asm_usb = None
+
+  def _open_asm_usb(self):
+    context = usb1.USBContext()
+    for vendor_id, product_id in CHESTNUT_USB_IDS:
+      if (handle := context.openByVendorIDAndProductID(vendor_id, product_id, skip_on_error=True)) is not None:
+        return handle
+    context.close()
+
+  def _read_ina(self) -> tuple[int, int, bool]:
+    if "AMD" in Device._opened_devices and self._asm_usb is None:
+      try:
+        raw = Device["AMD"].iface.pci_dev.usb.usb.control_read(0xC0, 5)
+        return struct.unpack('<Hh?', bytes(raw))
+      except Exception:
+        pass
+    if self._asm_usb is None:
+      self._asm_usb = self._open_asm_usb()
+    if self._asm_usb is None:
+      raise usb1.USBErrorNoDevice
+    try:
+      raw = self._asm_usb.controlRead(0xC0, 0xC0, 0, 0, 5, timeout=100)
+    except usb1.USBError:
+      self._close_asm_usb()
+      raise
+    return struct.unpack('<Hh?', bytes(raw))

  @cached_property
  def power_limit(self) -> int:
@@ -94,8 +127,10 @@ class ChestnutState:
    if self.big and "AMD" in Device._opened_devices and self.sends % 100 == 1:
      try:
        smu = Device["AMD"].iface.dev_impl.smu
+        metrics_t = smu.smu_mod.SmuMetricsExternal_t
        smu._send_msg(smu.smu_mod.PPSMC_MSG_TransferTableSmu2Dram, smu.smu_mod.TABLE_SMU_METRICS, timeout=100)
-        metrics = smu.read_table(smu.smu_mod.SmuMetricsExternal_t, smu.smu_mod.TABLE_SMU_METRICS).SmuMetrics
+        metrics_buf = bytearray(smu.adev.vram.view(smu.driver_table_paddr, ctypes.sizeof(metrics_t))[:])
+        metrics = metrics_t.from_buffer(metrics_buf).SmuMetrics
        self.metrics = {'tempC': metrics.AvgTemperature[smu.smu_mod.TEMP_HOTSPOT],
                        'memoryTempC': metrics.AvgTemperature[smu.smu_mod.TEMP_MEM],
                        'powerDrawW': metrics.AverageSocketPower,
@@ -114,13 +149,15 @@ class ChestnutState:
        setattr(state, k, v)

    asm_valid = False
+    try:
+      # ASM runs on USB-C power, these still read without a gpu
+      state.supplyVoltage, state.supplyCurrent, state.supplyFault = self._read_ina()
+      asm_valid = True
+    except Exception:
+      pass
    if "AMD" in Device._opened_devices:
      try:
-        # ASM runs on USB-C power, these still read without a gpu
-        asm = Device["AMD"].iface.pci_dev.usb
-        state.pcieLtssm = asm.read(0xB450, 1)[0]
-        state.supplyVoltage, state.supplyCurrent = struct.unpack('<Hh', bytes(asm.usb.control_read(0xC0, 5))[:4])
-        asm_valid = True
+        state.pcieLtssm = Device["AMD"].iface.pci_dev.usb.read(0xB450, 1)[0]
      except Exception:
        pass

@@ -141,42 +178,34 @@ class FrameMeta:
 class ModelState(ModelStateBase):
  prev_desire: np.ndarray  # for tracking the rising edge of the pulse

-  def __init__(self, cam_w: int, cam_h: int, usbgpu: bool):
+  def __init__(self, cam_w: int, cam_h: int, chestnut: bool):
    ModelStateBase.__init__(self)
-    input_devices = get_tg_input_devices(PROCESS_NAME, usbgpu)
-    self.WARP_DEV, self.QUEUE_DEV = input_devices['WARP_DEV'], input_devices['QUEUE_DEV']
-    jits = load_oob(open_file_chunked(modeld_pkl_path(usbgpu)))
+    jits = load_oob(open_file_chunked(modeld_pkl_path(chestnut)))
+    input_devices = jits['input_devices']
+    self.model_device = input_devices['model']
    metadata = jits['metadata']
    self.input_shapes = metadata['input_shapes']
    self.vision_input_names = [k for k in self.input_shapes if 'img' in k]
    self.output_slices = metadata['output_slices']

    self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
-    self.usbgpu = usbgpu
+    self.chestnut = chestnut

    self.frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
-    self.input_queues, self.npy = make_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
-    self.full_frames: dict[str, Tensor] = {}
-    self._blob_cache: dict[tuple[str, int], Tensor] = {}
+    self.frame_copy_size = nv12_copy_size(*get_nv12_info(cam_w, cam_h)[:3])
+    self.input_queues, self.npy, self.frame_views = make_input_queues(
+      self.input_shapes, self.frame_skip, device=self.model_device, frame_copy_size=self.frame_copy_size)
    self.parser = Parser()
-    self.frame_buf_params = {k: get_nv12_info(cam_w, cam_h) for k in ('img', 'big_img')}
-    self.run_policy = jits['run_policy']
-    self.warp = jits[(cam_w,cam_h)]
+    self.run_model = jits['run_model'][(cam_w,cam_h)]

  def slice_outputs(self, model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]:
    parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
    return parsed_model_outputs

  def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
-          inputs: dict[str, np.ndarray]) -> dict[str, np.ndarray] | None:
-    for key in bufs.keys():
-      ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
-      yuv_size = self.frame_buf_params[key][3]
-      # There is a ringbuffer of imgs, just cache tensors pointing to all of them
-      cache_key = (key, ptr)
-      if cache_key not in self._blob_cache:
-        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
-      self.full_frames[key] = self._blob_cache[cache_key]
+          inputs: dict[str, np.ndarray], after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray]:
+    for key, buf in bufs.items():
+      np.copyto(self.frame_views[key], np.frombuffer(buf.data, dtype=np.uint8, count=self.frame_copy_size))

    # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
    inputs['desire_pulse'][0] = 0
@@ -187,16 +216,12 @@ class ModelState(ModelStateBase):
    self.npy['tfm'][:,:] = transforms['img'][:,:]
    self.npy['big_tfm'][:,:] = transforms['big_img'][:,:]

-    warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames['img'], big_frame=self.full_frames['big_img'])
-
-    outs, = self.run_policy(
-      **{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped
-    )
+    outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
+    if after_enqueue is not None:
+      after_enqueue()
    model_output = outs.numpy()[0]
-    if self.usbgpu and not np.all(np.isfinite(model_output)):
-      # TODO remove with prev_feat
-      cloudlog.error("model output not finite, dropping frame")
-      return None
+    if self.chestnut and not np.all(np.isfinite(model_output)):
+      raise RuntimeError("model output not finite")
    outputs_dict = self.parser.parse_outputs(self.slice_outputs(model_output, self.output_slices))
    self.npy['prev_feat'][:] = model_output[self.output_slices['hidden_state']]

@@ -205,25 +230,37 @@ class ModelState(ModelStateBase):
    return outputs_dict

  def warmup(self) -> None:
-    dummy_frames = {k: np.zeros(self.frame_buf_params[k][3], dtype=np.uint8) for k in self.vision_input_names}
+    dummy_frames = {k: np.zeros(self.frame_copy_size, dtype=np.uint8) for k in self.vision_input_names}
    eye = np.eye(3, dtype=np.float32)
    dims = {'desire_pulse': ModelConstants.DESIRE_LEN, 'traffic_convention': 2, 'action_t': 2}
    self.run(dummy_frames, dict.fromkeys(self.vision_input_names, eye), {k: np.zeros(v, dtype=np.float32) for k, v in dims.items()})
-    self.input_queues, self.npy = make_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
+    self.input_queues, self.npy, self.frame_views = make_input_queues(
+      self.input_shapes, self.frame_skip, device=self.model_device, frame_copy_size=self.frame_copy_size)
    self.prev_desire[:] = 0
-    self.full_frames.clear()
-    self._blob_cache.clear()


 def main(demo=False):
  cloudlog.warning("modeld init")

-  USBGPU = usbgpu_present() and usbgpu_compiled()
-  if USBGPU:
+  chestnut_available = chestnut_present() and chestnut_compiled()
+  CHESTNUT = False
+  if chestnut_available:
+    poller = messaging.Poller()
+    sock = messaging.sub_sock("chestnutState", poller=poller, conflate=True)
+    deadline = time.monotonic() + 4. / SERVICE_LIST['deviceState'].frequency
+    while not CHESTNUT and (remaining := deadline - time.monotonic()) > 0.:
+      if not poller.poll(round(remaining * 1000)):
+        break
+      msg = messaging.recv_one_or_none(sock)
+      CHESTNUT = msg is not None and msg.valid and chestnut_ready(msg.chestnutState)
+  if CHESTNUT:
    os.environ['HCQDEV_WAIT_TIMEOUT_MS'] = '3000'
  params = Params()
-  params.put_bool("UsbGpuLoading", USBGPU)
-  params.remove("UsbGpuActive")
+  params.put_bool("ChestnutLoading", CHESTNUT)
+  if chestnut_available and not CHESTNUT:
+    params.put_bool("ChestnutActive", False)
+  else:
+    params.remove("ChestnutActive")

  config_realtime_process(7, 54)

@@ -253,7 +290,7 @@ def main(demo=False):
  st = time.monotonic()
  cloudlog.warning("loading model")
  model = None
-  if USBGPU:
+  if CHESTNUT:
    big_model = None
    def load_big():
      nonlocal big_model
@@ -267,23 +304,27 @@ def main(demo=False):
    loader.start()
    loader.join(BIG_MODEL_TIMEOUT)
    model = big_model
-    params.put_bool("UsbGpuActive", model is not None)
+    if model is None:
+      params.put_bool("ChestnutModelError", True)
+    params.put_bool("ChestnutActive", model is not None)
+    if model is not None:
+      params.remove("ChestnutModelError")

-  small_model = ModelState(vipc_client_main.width, vipc_client_main.height, False) if model is None or USBGPU else None
+  small_model = ModelState(vipc_client_main.width, vipc_client_main.height, False) if model is None or CHESTNUT else None
  if model is None:
    model = small_model
-  params.put_bool("UsbGpuLoading", False)
+  params.put_bool("ChestnutLoading", False)
  assert model is not None
  cloudlog.warning(f"models loaded in {time.monotonic() - st:.1f}s, modeld starting")

  # messaging
-  pub_socks = ["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"] + (["chestnutState"] if USBGPU else [])
+  pub_socks = ["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"] + (["chestnutState"] if CHESTNUT else [])
  pm = PubMaster(pub_socks)
  sm = SubMaster(["deviceState", "carState", "narrowRoadCameraState", "extrinsicsCalibration", "driverMonitoringState", "carControl", "lateralDelay"])

  publish_state = PublishState()
  params = Params()
-  chestnut_state = ChestnutState(pm, model.usbgpu) if USBGPU else None
+  chestnut_state = ChestnutState(pm, model.chestnut) if CHESTNUT else None

  # setup filter to track dropped frames
  frame_dropped_filter = FirstOrderFilter(0., 10., 1. / ModelConstants.MODEL_RUN_FREQ)
@@ -393,13 +434,16 @@ def main(demo=False):

    mt1 = time.perf_counter()
    try:
-      model_output = model.run(bufs, transforms, inputs)
+      send_chestnut = (chestnut_state is not None and
+                       run_count % round(ModelConstants.MODEL_RUN_FREQ / SERVICE_LIST['chestnutState'].frequency) == 0)
+      model_output = model.run(bufs, transforms, inputs, chestnut_state.send if send_chestnut else None)
    except Exception:
-      if not params.get_bool("UsbGpuActive"):
+      if not params.get_bool("ChestnutActive"):
        raise
      # fallback to small model
      cloudlog.exception("big model failed, fall back to small")
-      params.put_bool("UsbGpuActive", False)
+      params.put_bool("ChestnutModelError", True)
+      params.put_bool("ChestnutActive", False)
      assert small_model is not None
      model = small_model
      if chestnut_state is not None:
@@ -419,18 +463,17 @@ def main(demo=False):
      fill_model_msg(modelv2_send, model_output, action,
                     publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
                     frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, extrinsics_calibration_seen)
-      modelv2_send.modelV2.big = model.usbgpu
+      modelv2_send.modelV2.big = model.chestnut

      desire_state = modelv2_send.modelV2.meta.desireState
      l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
      r_lane_change_prob = desire_state[log.Desire.laneChangeRight]
      lane_change_prob = l_lane_change_prob + r_lane_change_prob
-      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob)
-      modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
-      modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
-
      mdv2sp_send = messaging.new_message('modelDataV2SP')
      left_edge, right_edge = RELC.update_and_fill(modelv2_send.modelV2, mdv2sp_send.modelDataV2SP, v_ego)
+      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, left_edge, right_edge)
+      modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
+      modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
      mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction

      fill_driving_model_data(drivingdata_send, modelv2_send)
@@ -441,10 +484,6 @@ def main(demo=False):
      pm.send('modelDataV2SP', mdv2sp_send)
    last_vipc_frame_id = meta_main.frame_id

-    if chestnut_state is not None and run_count % round(ModelConstants.MODEL_RUN_FREQ / SERVICE_LIST['chestnutState'].frequency) == 0:
-      chestnut_state.send()
-
-
 if __name__ == "__main__":
  try:
    import argparse
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a501760a9d1d5fef0eab2b8c5d122d06124fc26dc8e0782e0aa94b82a208f0ff
-size 1757355221
+oid sha256:1791d5940b2c048d0639813426dd2cf1d6f2a6727ed51e17c8bcea8bbe754123
+size 765950064
@@ -123,22 +123,22 @@ void fill_panda_state(cereal::PandaState::Builder &ps, cereal::PandaState::Panda
  ps.setUptime(health.uptime_pkt);
  ps.setSafetyTxBlocked(health.safety_tx_blocked_pkt);
  ps.setSafetyRxInvalid(health.safety_rx_invalid_pkt);
-  ps.setIgnitionLine(health.ignition_line_pkt);
-  ps.setIgnitionCan(health.ignition_can_pkt);
-  ps.setControlsAllowed(health.controls_allowed_pkt);
+  ps.setIgnitionLine((health.flags_pkt & HEALTH_FLAG_IGNITION_LINE) != 0U);
+  ps.setIgnitionCan((health.flags_pkt & HEALTH_FLAG_IGNITION_CAN) != 0U);
+  ps.setControlsAllowed((health.flags_pkt & HEALTH_FLAG_CONTROLS_ALLOWED) != 0U);
  ps.setTxBufferOverflow(health.tx_buffer_overflow_pkt);
  ps.setRxBufferOverflow(health.rx_buffer_overflow_pkt);
  ps.setPandaType(hw_type);
  ps.setSafetyModel(cereal::CarParams::SafetyModel(health.safety_mode_pkt));
  ps.setSafetyParam(health.safety_param_pkt);
  ps.setFaultStatus(cereal::PandaState::FaultStatus(health.fault_status_pkt));
-  ps.setPowerSaveEnabled((bool)(health.power_save_enabled_pkt));
-  ps.setHeartbeatLost((bool)(health.heartbeat_lost_pkt));
+  ps.setPowerSaveEnabled((health.flags_pkt & HEALTH_FLAG_POWER_SAVE_ENABLED) != 0U);
+  ps.setHeartbeatLost((health.flags_pkt & HEALTH_FLAG_HEARTBEAT_LOST) != 0U);
  ps.setAlternativeExperience(health.alternative_experience_pkt);
  ps.setHarnessStatus(cereal::PandaState::HarnessStatus(health.car_harness_status_pkt));
-  ps.setInterruptLoad(health.interrupt_load_pkt);
+  ps.setInterruptLoad(health.interrupt_load_pkt / 255.0f);
  ps.setFanPower(health.fan_power);
-  ps.setSafetyRxChecksInvalid((bool)(health.safety_rx_checks_invalid_pkt));
+  ps.setSafetyRxChecksInvalid((health.flags_pkt & HEALTH_FLAG_SAFETY_RX_CHECKS_INVALID) != 0U);
  ps.setSpiErrorCount(health.spi_error_count_pkt);
  ps.setSbu1Voltage(health.sbu1_voltage_mV / 1000.0f);
  ps.setSbu2Voltage(health.sbu2_voltage_mV / 1000.0f);
@@ -198,10 +198,10 @@ std::optional<bool> send_panda_states(PubMaster *pm, Panda *panda, bool is_onroa
  }

  if (spoofing_started) {
-    health.ignition_line_pkt = 1;
+    health.flags_pkt |= HEALTH_FLAG_IGNITION_LINE;
  }

-  bool ignition_local = ((health.ignition_line_pkt != 0) || (health.ignition_can_pkt != 0)) && !always_offroad;
+  bool ignition_local = ((health.flags_pkt & (HEALTH_FLAG_IGNITION_LINE | HEALTH_FLAG_IGNITION_CAN)) != 0U) && !always_offroad;

  // Make sure CAN buses are live: safety_setter_thread does not work if Panda CAN are silent and there is only one other CAN node
  if (health.safety_mode_pkt == (uint8_t)(cereal::CarParams::SafetyModel::SILENT)) {
@@ -209,7 +209,7 @@ std::optional<bool> send_panda_states(PubMaster *pm, Panda *panda, bool is_onroa
  }

  bool power_save_desired = !ignition_local;
-  if (health.power_save_enabled_pkt != power_save_desired) {
+  if (((health.flags_pkt & HEALTH_FLAG_POWER_SAVE_ENABLED) != 0U) != power_save_desired) {
    panda->set_power_saving(power_save_desired);
  }

@@ -19,6 +19,30 @@
  },
  "Offroad_ChestnutBranch": {
    "text": "Chestnut detected! Switch to the %1 branch to use chestnut-class models.",
+    "severity": -1
+  },
+  "Offroad_ChestnutNotDetected": {
+    "text": "Chestnut not detected. Check USB and 12V connections.",
+    "severity": 0
+  },
+  "Offroad_ChestnutOverheated": {
+    "text": "Chestnut overheated. Ensure good airflow. Current GPU temperature is %1.",
+    "severity": 0
+  },
+  "Offroad_ChestnutPcieUnavailable": {
+    "text": "%1",
+    "severity": 0
+  },
+  "Offroad_ChestnutUncompiled": {
+    "text": "Chestnut model not compiled. Keep ignition on and reboot the comma.",
+    "severity": 0
+  },
+  "Offroad_ChestnutUpdateFailed": {
+    "text": "Chestnut update failed. Check the USB cable.",
+    "severity": 0
+  },
+  "Offroad_ChestnutUsbSlow": {
+    "text": "Chestnut USB link is slow. Check the USB cable. The current speed is %1.",
    "severity": 0
  },
  "Offroad_UnregisteredHardware": {
@@ -32,7 +32,14 @@ from openpilot.sunnypilot.selfdrive.car.car_specific import CarSpecificEventsSP
 from openpilot.sunnypilot.selfdrive.car.cruise_helpers import CruiseHelper
 from openpilot.sunnypilot.selfdrive.car.intelligent_cruise_button_management.controller import IntelligentCruiseButtonManagement
 from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
+from openpilot.sunnypilot.selfdrive.selfdrived.assisted_driving_milestones import (
+  AssistCategory,
+  AssistedDrivingMilestones,
+  MilestoneEvent,
+  MilestoneStore,
+)
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP
+from openpilot.sunnypilot.system.statsd import statlog

 REPLAY = "REPLAY" in os.environ
 SIMULATION = "SIMULATION" in os.environ
@@ -88,7 +95,8 @@ class SelfdriveD(CruiseHelper):
    self.big_model_ready_t = 0.

    # Setup sockets
-    self.pm = messaging.PubMaster(['selfdriveState', 'onroadEvents'] + ['selfdriveStateSP', 'onroadEventsSP'])
+    self.pm = messaging.PubMaster(['selfdriveState', 'onroadEvents'] +
+                                  ['selfdriveStateSP', 'onroadEventsSP', 'assistedDrivingMilestoneState'])

    self.gps_location_service = get_gps_location_service(self.params)
    self.gps_packets = [self.gps_location_service]
@@ -127,6 +135,7 @@ class SelfdriveD(CruiseHelper):
      self.params.remove("ExperimentalMode")

    self.CS_prev = car.CarState.new_message()
+    self.car_state_log_mono_time = 0
    self.AM = AlertManager()
    self.events = Events()

@@ -137,6 +146,11 @@ class SelfdriveD(CruiseHelper):
    self.cruise_mismatch_counter = 0
    self.last_steering_pressed_frame = 0
    self.distance_traveled = 0
+    self.assisted_driving_milestones = AssistedDrivingMilestones(MilestoneStore(self.params))
+    self.assisted_driving_milestones_enabled = bool(self.params.get("AssistedDrivingMilestonesEnabled", return_default=True))
+    self.assisted_driving_milestone_drive_id = ""
+    self._milestone_event: MilestoneEvent | None = None
+    self._milestone_event_expires_ns = 0
    self.last_functional_fan_frame = 0
    self.events_prev = []
    self.logged_comm_issue = None
@@ -195,17 +209,18 @@ class SelfdriveD(CruiseHelper):
      self.events.add(EventName.joystickDebug)
      self.startup_event = None

-    loading = self.params.get_bool("UsbGpuLoading")
+    loading = self.params.get_bool("ChestnutLoading")
    if self.big_model_loading and not loading:
      self.big_model_ready_t = time.monotonic()
+      self.events_sp.add(custom.OnroadEventSP.EventName.bigModelReady)
    self.big_model_loading = loading
    if self.big_model_loading:
      self.events.add(EventName.bigModelLoading)

-    big_active = self.params.get("UsbGpuActive")
-    usbgpu_present = self.sm['deviceState'].chestnutPresent
+    big_active = self.params.get("ChestnutActive")
+    chestnut_present = self.sm['deviceState'].chestnutPresent
    model_unavailable = big_active is True and self.sm.seen['modelV2'] and not self.sm.alive['modelV2']
-    big_failed = big_active is False or model_unavailable or (self.big_model_active and not usbgpu_present)
+    big_failed = big_active is False or model_unavailable or (self.big_model_active and not chestnut_present)
    if big_failed and not self.big_model_failed:
      self.events.add(EventName.bigModelFailed)
    self.big_model_failed = big_failed
@@ -527,6 +542,8 @@ class SelfdriveD(CruiseHelper):
  def data_sample(self):
    _car_state = messaging.recv_one(self.car_state_sock)
    CS = _car_state.carState if _car_state else self.CS_prev
+    if _car_state is not None:
+      self.car_state_log_mono_time = _car_state.logMonoTime

    self.sm.update(0)

@@ -645,6 +662,31 @@ class SelfdriveD(CruiseHelper):
      self.pm.send('onroadEventsSP', ce_send_sp)
    self.events_sp_prev = self.events_sp.names.copy()

+  def publish_assisted_driving_milestones(self, now_ns: int, event: MilestoneEvent | None) -> None:
+    if event is not None:
+      self._milestone_event = event
+      self._milestone_event_expires_ns = now_ns + 1_000_000_000
+    elif now_ns >= self._milestone_event_expires_ns:
+      self._milestone_event = None
+
+    if event is None and self.sm.frame % 10 != 0:
+      return
+
+    snapshot = self.assisted_driving_milestones.snapshot()
+    msg = messaging.new_message("assistedDrivingMilestoneState")
+    msg.valid = True
+    state = msg.assistedDrivingMilestoneState
+    state.enabled = self.assisted_driving_milestones_enabled
+    state.madsDistanceMeters = snapshot.distances_meters[AssistCategory.MADS]
+    state.fullAssistDistanceMeters = snapshot.distances_meters[AssistCategory.FULL_ASSIST]
+    if self._milestone_event is not None:
+      state.event.id = self._milestone_event.event_id
+      state.event.category = self._milestone_event.category.value
+      state.event.distanceMeters = self._milestone_event.distance_meters
+      state.event.previousDistanceMeters = self._milestone_event.previous_distance_meters
+      state.event.unit = self._milestone_event.unit.value
+    self.pm.send("assistedDrivingMilestoneState", msg)
+
  def step(self):
    CS = self.data_sample()
    self.update_events(CS)
@@ -654,6 +696,28 @@ class SelfdriveD(CruiseHelper):
      self.mads.update(CS)
    self.update_alerts(CS)

+    now_ns = time.monotonic_ns()
+    if not self.assisted_driving_milestone_drive_id:
+      self.assisted_driving_milestone_drive_id = self.params.get("CurrentRoute") or ""
+      self.assisted_driving_milestones.set_drive_id(self.assisted_driving_milestone_drive_id)
+    car_control = self.sm['carControl']
+    milestone_event = self.assisted_driving_milestones.update(
+      self.car_state_log_mono_time,
+      CS.vEgo,
+      lat_active=car_control.latActive,
+      long_active=car_control.longActive,
+      is_metric=self.is_metric,
+      enabled=self.assisted_driving_milestones_enabled,
+    )
+    if milestone_event is not None:
+      cloudlog.event("assisted_driving_milestone_reached",
+                     event_id=milestone_event.event_id,
+                     category=milestone_event.category.value,
+                     distance_meters=milestone_event.distance_meters)
+      statlog.gauge(f"assisted_driving_milestone.{milestone_event.category.value}.meters",
+                    milestone_event.distance_meters)
+    self.publish_assisted_driving_milestones(now_ns, milestone_event)
+
    self.button_state_tracker.update(CS)
    self.publish_selfdriveState(CS)

@@ -666,6 +730,7 @@ class SelfdriveD(CruiseHelper):
      self.disengage_on_accelerator = self.params.get_bool("DisengageOnAccelerator")
      self.experimental_mode = self.params.get_bool("ExperimentalMode") and self.CP.openpilotLongitudinalControl
      self.personality = self.params.get("LongitudinalPersonality", return_default=True)
+      self.assisted_driving_milestones_enabled = bool(self.params.get("AssistedDrivingMilestonesEnabled", return_default=True))

      self.mads.read_params()
      time.sleep(0.1)
@@ -679,6 +744,7 @@ class SelfdriveD(CruiseHelper):
        self.step()
        self.rk.monitor_time()
    finally:
+      self.assisted_driving_milestones.close()
      e.set()
      t.join()

@@ -152,7 +152,7 @@ def migrate_drivingModelData(msgs):
  add_ops = []
  for _, msg in msgs:
    dmd = messaging.new_message('drivingModelData', valid=msg.valid, logMonoTime=msg.logMonoTime)
-    for field in ["frameId", "frameIdExtra", "frameDropPerc", "modelExecutionTime", "action"]:
+    for field in ["frameId", "frameIdExtra", "frameDropPerc", "modelExecutionTime", "big", "action"]:
      setattr(dmd.drivingModelData, field, getattr(msg.modelV2, field))
    for meta_field in ["laneChangeState", "laneChangeState"]:
      setattr(dmd.drivingModelData.meta, meta_field, getattr(msg.modelV2.meta, meta_field))
@@ -33,9 +33,9 @@ MODEL_REPLAY_BUCKET="model_replay_master"
 GITHUB = GithubUtils(API_TOKEN, DATA_TOKEN)

 EXEC_TIMINGS = [
-  # model, instant max, average max
-  ("modelV2", 0.05, 0.028),
-  ("driverStateV2", 0.05, 0.018),
+  # model, instant max, average max, chestnut average max
+  ("modelV2", 0.05, 0.03, 0.05),
+  ("driverStateV2", 0.05, 0.018, 0.018),
 ]

 def get_log_fn(test_route, ref="master"):
@@ -169,11 +169,13 @@ def model_replay(lr, frs):
  dmonitoringmodeld_msgs = replay_process(dmonitoringmodeld, dmodeld_logs, frs)

  msgs = modeld_msgs + dmonitoringmodeld_msgs
+  chestnut = any(m.modelV2.big for m in modeld_msgs if m.which() == "modelV2")

  header = ['model', 'max instant', 'max instant allowed', 'average', 'max average allowed', 'test result']
  rows = []
  timings_ok = True
-  for (s, instant_max, avg_max) in EXEC_TIMINGS:
+  for (s, instant_max, avg_max, chestnut_avg_max) in EXEC_TIMINGS:
+    avg_max = chestnut_avg_max if chestnut else avg_max
    ts = [getattr(m, s).modelExecutionTime for m in msgs if m.which() == s]
    # TODO some init can happen in first iteration
    ts = ts[1:]
@@ -1,7 +1,7 @@
 import time
 import pyray as rl

-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.selfdrive.ui.ui_state import ui_state
@@ -26,8 +26,8 @@ class BodyLayout(Widget):
    self._last_input_time = time.monotonic()
    self._was_active = False
    self._offroad_label = UnifiedLabel("turn on ignition to use", 95 if gui_app.big_ui() else 45, FontWeight.DISPLAY,
-                                       alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                       alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                                       alignment=TextAlignment.CENTER,
+                                       alignment_vertical=TextAlignmentVertical.MIDDLE)

  def draw_dot_grid(self, rect: rl.Rectangle, dots: list[tuple[int, int]], color: rl.Color):
    spacing = min(rect.height / GRID_ROWS, rect.width / GRID_COLS)
@@ -8,7 +8,7 @@ from openpilot.selfdrive.ui.widgets.exp_mode_button import ExperimentalModeButto
 from openpilot.selfdrive.ui.widgets.prime import PrimeWidget
 from openpilot.selfdrive.ui.widgets.setup import SetupWidget
 from openpilot.system.ui.lib.text_measure import measure_text_cached
-from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos
+from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos, TextAlignment
 from openpilot.system.ui.lib.multilang import tr, trn
 from openpilot.system.ui.widgets.label import gui_label
 from openpilot.system.ui.widgets import Widget
@@ -178,7 +178,7 @@ class HomeLayout(Widget):

    version_rect = rl.Rectangle(self.header_rect.x + self.header_rect.width - version_text_width, self.header_rect.y,
                                version_text_width, self.header_rect.height)
-    gui_label(version_rect, self._version_text, 48, rl.WHITE, alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+    gui_label(version_rect, self._version_text, 48, rl.WHITE, alignment=TextAlignment.RIGHT)

  def _render_home_content(self):
    self._render_left_column()
@@ -5,7 +5,7 @@ from enum import IntEnum

 import pyray as rl
 from openpilot.common.basedir import BASEDIR
-from openpilot.system.ui.lib.application import FontWeight, gui_app
+from openpilot.system.ui.lib.application import FontWeight, TextAlignment, gui_app
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.button import Button, ButtonStyle
@@ -115,9 +115,9 @@ class TermsPage(Widget):
    self._on_accept = on_accept
    self._on_decline = on_decline

-    self._title = Label(tr("Welcome to sunnypilot"), font_size=90, font_weight=FontWeight.BOLD, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT)
+    self._title = Label(tr("Welcome to sunnypilot"), font_size=90, font_weight=FontWeight.BOLD, text_alignment=TextAlignment.LEFT)
    self._desc = Label(tr("You must accept the Terms of Service to use sunnypilot. Read the latest terms at https://sunnypilot.ai/terms before continuing."),
-                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT)
+                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=TextAlignment.LEFT)

    self._decline_btn = Button(tr("Decline"), click_callback=on_decline)
    self._accept_btn = Button(tr("Agree"), button_style=ButtonStyle.PRIMARY, click_callback=on_accept)
@@ -150,7 +150,7 @@ class DeclinePage(Widget):
  def __init__(self, back_callback=None):
    super().__init__()
    self._text = Label(tr("You must accept the Terms of Service in order to use sunnypilot."),
-                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT)
+                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=TextAlignment.LEFT)
    self._back_btn = Button(tr("Back"), click_callback=back_callback)
    self._uninstall_btn = Button(tr("Decline, uninstall sunnypilot"), button_style=ButtonStyle.DANGER,
                                 click_callback=self._on_uninstall_clicked)
@@ -199,6 +199,9 @@ class SoftwareLayout(Widget):
        selection = self._branch_dialog.selection
        ui_state.params.put("UpdaterTargetBranch", selection, block=True)
        self._branch_btn.action_item.set_value(selection)
+        self._download_btn.action_item.set_enabled(False)
+        self._waiting_for_updater = True
+        self._waiting_start_ts = time.monotonic()
        subprocess.run("pkill -SIGUSR1 -f openpilot.system.updated.updated", shell=True)
      self._branch_dialog = None

@@ -168,9 +168,16 @@ class Sidebar(Widget, SidebarSP):
    # Home/Flag button
    flag_pressed = mouse_down and rl.check_collision_point_rec(mouse_pos, HOME_BTN)
    button_img = self._flag_img if ui_state.started else self._home_img
+    button_pos = rl.Vector2(HOME_BTN.x, HOME_BTN.y)
+    icon_opacity = 1.0
+
+    if gui_app.sunnypilot_ui():
+      button_img, button_pos, icon_opacity = SidebarSP._get_home_icon(self, button_img)

    tint = Colors.BUTTON_PRESSED if (ui_state.started and flag_pressed) else Colors.BUTTON_NORMAL
-    rl.draw_texture_ex(button_img, rl.Vector2(HOME_BTN.x, HOME_BTN.y), 0.0, 1.0, tint)
+    if icon_opacity < 1.0:
+      tint = rl.Color(tint[0], tint[1], tint[2], int(255 * icon_opacity))
+    rl.draw_texture_ex(button_img, button_pos, 0.0, 1.0, tint)

    # Microphone button
    if self._recording_audio:
@@ -1,4 +1,5 @@
 import datetime
+import math
 import time

 from openpilot.cereal import log
@@ -8,8 +9,8 @@ from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.layouts import HBoxLayout
 from openpilot.system.ui.widgets.icon_widget import IconWidget
 from openpilot.system.ui.widgets.label import UnifiedLabel, gui_label
-from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos
-from openpilot.selfdrive.ui.ui_state import ui_state
+from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos, TextAlignment, TextAlignmentVertical
+from openpilot.selfdrive.ui.ui_state import ui_state, ChestnutState
 from openpilot.common.version import RELEASE_BRANCHES

 HEAD_BUTTON_FONT_SIZE = 40
@@ -69,8 +70,8 @@ class AlertsPill(Widget):

      count_rect = rl.Rectangle(self.rect.x + self.COUNT_OFFSET, self.rect.y, pill_w - self.COUNT_OFFSET, pill_h)
      gui_label(count_rect, str(alert_count), font_size=36,
-                alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                alignment=TextAlignment.CENTER,
+                alignment_vertical=TextAlignmentVertical.MIDDLE)


 class NetworkIcon(Widget):
@@ -139,8 +140,10 @@ class MiciHomeLayout(Widget):
    self._version_text = self._get_version_text()

    self._experimental_icon = IconWidget("icons_mici/experimental_mode.png", (48, 48))
-    self._egpu_icon = IconWidget("icons_mici/egpu_green.png", (50, 37))
-    self._egpu_icon_gray = IconWidget("icons_mici/egpu_gray.png", (50, 37))
+    self._usb_icon = IconWidget("icons_mici/usb.png", (62, 40))
+    self._chestnut_icon = IconWidget("icons_mici/chestnut_green.png", (68, 40))
+    self._chestnut_loading_icon = IconWidget("icons_mici/chestnut.png", (68, 40))
+    self._chestnut_failed_icon = IconWidget("icons_mici/chestnut_orange.png", (68, 40))
    self._mic_icon = IconWidget("icons_mici/microphone.png", (32, 46))
    self._body_icon = IconWidget("icons_mici/body.png", (54, 37))

@@ -150,13 +153,15 @@ class MiciHomeLayout(Widget):
      IconWidget("icons_mici/settings.png", (48, 48), opacity=0.9),
      NetworkIcon(),
      self._experimental_icon,
-      self._egpu_icon,
-      self._egpu_icon_gray,
+      self._usb_icon,
+      self._chestnut_icon,
+      self._chestnut_loading_icon,
+      self._chestnut_failed_icon,
      self._body_icon,
      self._mic_icon,
    ], spacing=18)

-    self._openpilot_label = UnifiedLabel("sunnypilot", font_size=96, font_weight=FontWeight.DISPLAY, max_width=480, wrap_text=False)
+    self._openpilot_label = UnifiedLabel("openpilot", font_size=96, font_weight=FontWeight.DISPLAY, max_width=480, wrap_text=False)
    self._version_label = UnifiedLabel("", font_size=36, font_weight=FontWeight.ROMAN, max_width=480, wrap_text=False)
    self._large_version_label = UnifiedLabel("", font_size=64, text_color=rl.GRAY, font_weight=FontWeight.ROMAN, max_width=480, wrap_text=False)
    self._date_label = UnifiedLabel("", font_size=36, text_color=rl.GRAY, font_weight=FontWeight.ROMAN, max_width=480, wrap_text=False)
@@ -247,9 +252,20 @@ class MiciHomeLayout(Widget):
        self._version_commit_label.render()

    # ***** Center-aligned bottom section icons *****
+    usb_connected = ui_state.usb_connected
+    usb_unknown = ui_state.usb_unknown
+    chestnut_state = ui_state.chestnut_state
    self._experimental_icon.set_visible(ui_state.experimental_mode)
-    self._egpu_icon.set_visible(ui_state.sm["deviceState"].chestnutPresent and ui_state.usbgpu_compiled)
-    self._egpu_icon_gray.set_visible(ui_state.sm["deviceState"].chestnutPresent and not ui_state.usbgpu_compiled)
+    if gui_app.sunnypilot_ui():
+      self._set_chestnut_visibility()
+    else:
+      self._usb_icon.set_visible(usb_connected and usb_unknown)
+      self._chestnut_icon.set_visible(not usb_unknown and chestnut_state not in
+                                      (ChestnutState.LOADING, ChestnutState.UNCOMPILED, ChestnutState.FAILED) and
+                                      (usb_connected or chestnut_state in (ChestnutState.READY, ChestnutState.ACTIVE)))
+      self._chestnut_loading_icon.set_visible(not usb_unknown and chestnut_state == ChestnutState.LOADING)
+      self._chestnut_loading_icon.set_opacity(0.35 + 0.65 * (0.5 - 0.5 * math.cos(rl.get_time() * 6.0)))
+      self._chestnut_failed_icon.set_visible(not usb_unknown and chestnut_state in (ChestnutState.UNCOMPILED, ChestnutState.FAILED))
    self._mic_icon.set_visible(ui_state.recording_audio)
    self._body_icon.set_visible(bool(ui_state.is_body))

@@ -1,5 +1,8 @@
+import os
+
 import pyray as rl
 import openpilot.cereal.messaging as messaging
+from openpilot.common.hardware import PC
 from openpilot.selfdrive.ui.mici.layouts.home import MiciHomeLayout
 from openpilot.selfdrive.ui.mici.layouts.settings.settings import SettingsLayout
 from openpilot.selfdrive.ui.mici.layouts.offroad_alerts import MiciOffroadAlerts
@@ -61,7 +64,8 @@ class MiciMainLayout(Scroller):

    # Start onboarding if terms or training not completed, make sure to push after self
    self._onboarding_window = OnboardingWindow(lambda: gui_app.pop_widgets_to(self))
-    if not self._onboarding_window.completed:
+    skip_onboarding_for_milestone_preview = PC and os.getenv("SP_MILESTONE_PREVIEW") == "1"
+    if not self._onboarding_window.completed and not skip_onboarding_for_milestone_preview:
      gui_app.push_widget(self._onboarding_window)

    # initialize correct onroad layout
@@ -119,6 +123,8 @@ class MiciMainLayout(Scroller):
        self._onroad_time_delay = rl.get_time()
      else:
        self._scroll_to(self._home_layout)
+        if hasattr(self._home_layout, "request_drive_summary"):
+          self._home_layout.request_drive_summary()

    # FIXME: these two pops can interrupt user interacting in the settings
    if self._onroad_time_delay is not None and rl.get_time() - self._onroad_time_delay >= ONROAD_DELAY:
@@ -11,7 +11,7 @@ from openpilot.common.hardware import HARDWARE
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets.scroller import Scroller
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr

 REFRESH_INTERVAL = 5.0  # seconds
@@ -62,12 +62,12 @@ class AlertItem(Widget):
    self._icon_green = gui_app.texture("icons_mici/offroad_alerts/green_wheel.png", self.ICON_SIZE, self.ICON_SIZE)

    self._title_label = UnifiedLabel(text="", font_size=32, font_weight=FontWeight.SEMI_BOLD, text_color=self.TEXT_COLOR,
-                                     alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT,
-                                     alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP, line_height=0.95)
+                                     alignment=TextAlignment.LEFT,
+                                     alignment_vertical=TextAlignmentVertical.TOP, line_height=0.95)

    self._body_label = UnifiedLabel(text="", font_size=28, font_weight=FontWeight.ROMAN, text_color=self.TEXT_COLOR,
-                                    alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT,
-                                    alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM, line_height=0.95)
+                                    alignment=TextAlignment.LEFT,
+                                    alignment_vertical=TextAlignmentVertical.BOTTOM, line_height=0.95)

    self._title_text = ""
    self._body_text = ""
@@ -200,8 +200,8 @@ class MiciOffroadAlerts(Scroller):

    # Create empty state label
    self._empty_label = UnifiedLabel(tr("no alerts"), 65, FontWeight.DISPLAY, rl.WHITE,
-                                     alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                     alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                                     alignment=TextAlignment.CENTER,
+                                     alignment_vertical=TextAlignmentVertical.MIDDLE)

    # Build initial alert list
    self._build_alerts()
@@ -4,7 +4,7 @@ import pyray as rl
 from collections.abc import Callable
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.common.qrcode import make_texture
-from openpilot.system.ui.lib.application import FontWeight, gui_app
+from openpilot.system.ui.lib.application import FontWeight, gui_app, TextAlignment
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.button import SmallCircleIconButton
 from openpilot.system.ui.widgets.scroller import NavScroller, Scroller
@@ -35,7 +35,7 @@ class DriverCameraSetupDialog(BaseCabinCameraDialog):

    if not self._camera_view.frame:
      gui_label(rect, tr("camera starting"), font_size=64, font_weight=FontWeight.BOLD,
-                alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER)
+                alignment=TextAlignment.CENTER)
      rl.end_scissor_mode()
      return

@@ -74,6 +74,10 @@ class SoftwareInfoLayoutMici(Widget):


 class CheckUpdateButton(BigButton):
+  UPDATER_PROC = "openpilot.system.updated.updated"
+  CHECK_FOR_UPDATE = "SIGUSR1"
+  DOWNLOAD_UPDATE = "SIGHUP"
+
  def __init__(self):
    self._txt_update_icon = gui_app.texture("icons_mici/settings/device/update.png", 64, 75)
    self._txt_up_to_date_icon = gui_app.texture("icons_mici/settings/device/up_to_date.png", 64, 64)
@@ -97,15 +101,20 @@ class CheckUpdateButton(BigButton):
      gui_app.push_widget(dlg)
      return

+    self._signal_updater(self.DOWNLOAD_UPDATE if self.get_value() == "download update" else self.CHECK_FOR_UPDATE)
+
+  def check_for_update(self):
+    self._signal_updater(self.CHECK_FOR_UPDATE)
+
+  def _signal_updater(self, sig: str):
    self.set_enabled(False)
    self._state = UpdaterState.WAITING_FOR_UPDATER
+    self._hide_value_t = None
+    self.set_value("")
    self.set_icon(self._txt_update_icon)

    def run():
-      if self.get_value() == "download update":
-        subprocess.run("pkill -SIGHUP -f openpilot.system.updated.updated", shell=True)
-      else:
-        subprocess.run("pkill -SIGUSR1 -f openpilot.system.updated.updated", shell=True)
+      subprocess.run(f"pkill -{sig} -f {self.UPDATER_PROC}", shell=True)

    threading.Thread(target=run, daemon=True).start()

@@ -184,7 +193,7 @@ class CheckUpdateButton(BigButton):

 class InstallUpdateButton(BigButton):
  def __init__(self):
-    super().__init__("install update", "", gui_app.texture("icons_mici/settings/device/reboot.png", 64, 70))
+    super().__init__("install now", "", gui_app.texture("icons_mici/settings/device/reboot.png", 64, 70))
    self.set_visible(lambda: ui_state.is_offroad() and ui_state.params.get_bool("UpdateAvailable"))

  def _update_state(self):
@@ -232,8 +241,9 @@ class BranchSelectPage(NavScroller):


 class TargetBranchButton(BigButton):
-  def __init__(self):
+  def __init__(self, check_update_btn: CheckUpdateButton):
    super().__init__("target branch", ui_state.params.get("UpdaterTargetBranch") or "")
+    self._check_update_btn = check_update_btn
    self.set_click_callback(self._on_click)
    self.set_visible(not ui_state.params.get_bool("IsTestedBranch"))
    self.set_enabled(lambda: ui_state.is_offroad())
@@ -246,12 +256,15 @@ class TargetBranchButton(BigButton):
      self.set_value(target)

  def _on_click(self):
+    if not ui_state.params.get("UpdaterAvailableBranches"):
+      gui_app.push_widget(BigDialog("", tr("Failed to get available branches. Ensure you're connected to the internet and try again.")))
+      return
    gui_app.push_widget(BranchSelectPage(self._on_select))

  def _on_select(self, branch: str):
    ui_state.params.put("UpdaterTargetBranch", branch, block=True)
    self.set_value(branch)
-    subprocess.run("pkill -SIGUSR1 -f openpilot.system.updated.updated", shell=True)
+    self._check_update_btn.check_for_update()


 class SoftwareLayoutMici(NavScroller):
@@ -265,10 +278,11 @@ class SoftwareLayoutMici(NavScroller):
                                                        gui_app.texture("icons_mici/settings/device/uninstall.png", 64, 64),
                                                        uninstall_openpilot_callback, exit_on_confirm=False)

+    check_update_btn = CheckUpdateButton()
    self._scroller.add_widgets([
      SoftwareInfoLayoutMici(),
-      CheckUpdateButton(),
+      check_update_btn,
      InstallUpdateButton(),
-      TargetBranchButton(),
+      TargetBranchButton(check_update_btn),
      uninstall_openpilot_btn,
    ])
@@ -47,6 +47,7 @@ class TogglesLayoutMici(NavScroller):
    is_metric_toggle = BigParamControl("use metric units", "IsMetric")
    ldw_toggle = BigParamControl("lane departure warnings", "IsLdwEnabled")
    always_on_dm_toggle = BigParamControl("always-on driver monitor", "AlwaysOnDM")
+    milestone_celebrations_toggle = BigParamControl("assisted driving milestones", "AssistedDrivingMilestonesEnabled")
    record_front = BigParamControl("record & upload cabin camera", "RecordFront", toggle_callback=restart_needed_callback)
    record_mic = BigParamControl("record & upload mic audio", "RecordAudio", toggle_callback=restart_needed_callback)
    enable_openpilot = BigParamControl("enable sunnypilot", "OpenpilotEnabledToggle", toggle_callback=restart_needed_callback)
@@ -57,6 +58,7 @@ class TogglesLayoutMici(NavScroller):
      is_metric_toggle,
      ldw_toggle,
      always_on_dm_toggle,
+      milestone_celebrations_toggle,
      record_front,
      record_mic,
      enable_openpilot,
@@ -68,6 +70,7 @@ class TogglesLayoutMici(NavScroller):
      ("IsMetric", is_metric_toggle),
      ("IsLdwEnabled", ldw_toggle),
      ("AlwaysOnDM", always_on_dm_toggle),
+      ("AssistedDrivingMilestonesEnabled", milestone_celebrations_toggle),
      ("RecordFront", record_front),
      ("RecordAudio", record_mic),
      ("OpenpilotEnabledToggle", enable_openpilot),
@@ -10,7 +10,7 @@ from opendbc.car.structs import car
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.common.filter_simple import BounceFilter, FirstOrderFilter
 from openpilot.common.hardware import COMMA_HARDWARE
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel

@@ -20,6 +20,7 @@ AlertSize = log.SelfdriveState.AlertSize
 AlertStatus = log.SelfdriveState.AlertStatus

 ALERT_MARGIN = 18
+ALERT_BACKGROUND_OPACITY = 0.90

 ALERT_FONT_SMALL = 66 - 50
 ALERT_FONT_BIG = 88 - 40
@@ -279,7 +280,7 @@ class AlertRenderer(Widget, SpeedLimitAlertRenderer):
  def _draw_background(self, alert: Alert) -> None:
    # draw top gradient for alert text at top
    color = ALERT_COLORS.get(alert.status, ALERT_COLORS[AlertStatus.normal])
-    color = rl.Color(color.r, color.g, color.b, int(255 * 0.90 * self._alpha_filter.x))
+    color = rl.Color(color.r, color.g, color.b, int(255 * ALERT_BACKGROUND_OPACITY * self._alpha_filter.x))
    translucent_color = rl.Color(color.r, color.g, color.b, int(0 * self._alpha_filter.x))

    small_alert_height = round(self._rect.height * 0.583) # 140px at mici height
@@ -333,7 +334,7 @@ class AlertRenderer(Widget, SpeedLimitAlertRenderer):
    self._alert_text1_label.set_text(alert_text1)
    self._alert_text1_label.set_text_color(color)
    self._alert_text1_label.set_font_size(font_size)
-    self._alert_text1_label.set_alignment(rl.GuiTextAlignment.TEXT_ALIGN_LEFT if icon_side != 'left' else rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+    self._alert_text1_label.set_alignment(TextAlignment.LEFT if icon_side != 'left' else TextAlignment.RIGHT)
    self._alert_text1_label.render(text_rect1)

    alert_text2 = alert.text2.lower()
@@ -365,5 +366,5 @@ class AlertRenderer(Widget, SpeedLimitAlertRenderer):
      self._alert_text2_label.set_text(alert_text2)
      self._alert_text2_label.set_text_color(color)
      self._alert_text2_label.set_font_size(small_font_size)
-      self._alert_text2_label.set_alignment(rl.GuiTextAlignment.TEXT_ALIGN_LEFT if icon_side != 'left' else rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+      self._alert_text2_label.set_alignment(TextAlignment.LEFT if icon_side != 'left' else TextAlignment.RIGHT)
      self._alert_text2_label.render(text_rect2)
@@ -11,7 +11,7 @@ from openpilot.selfdrive.ui.mici.onroad.hud_renderer import HudRenderer
 from openpilot.selfdrive.ui.mici.onroad.model_renderer import ModelRenderer
 from openpilot.selfdrive.ui.mici.onroad.confidence_ball import ConfidenceBall
 from openpilot.selfdrive.ui.mici.onroad.cameraview import CameraView
-from openpilot.system.ui.lib.application import FontWeight, gui_app, MousePos, MouseEvent
+from openpilot.system.ui.lib.application import FontWeight, gui_app, MousePos, MouseEvent, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets import Widget
 from openpilot.common.filter_simple import BounceFilter
@@ -19,10 +19,15 @@ from openpilot.common.transformations.camera import DEVICE_CAMERAS, DeviceCamera
 from openpilot.common.transformations.orientation import rot_from_euler
 from enum import IntEnum

+MILESTONE_CELEBRATION_ENABLED = gui_app.sunnypilot_ui()
+
 if gui_app.sunnypilot_ui():
  from openpilot.selfdrive.ui.sunnypilot.mici.onroad.hud_renderer import HudRendererSP as HudRenderer
  from openpilot.selfdrive.ui.sunnypilot.ui_state import OnroadTimerStatus

+  if MILESTONE_CELEBRATION_ENABLED:
+    from openpilot.selfdrive.ui.sunnypilot.onroad.milestone_celebration import MilestoneCelebration
+
 OpState = log.SelfdriveState.OpenpilotState
 CALIBRATED = log.ExtrinsicsCalibration.Status.calibrated
 NARROW_ROAD_CAM = VisionStreamType.VISION_STREAM_NARROW_ROAD
@@ -156,10 +161,11 @@ class AugmentedRoadView(CameraView):
    self._alert_renderer = AlertRenderer()
    self._driver_state_renderer = DriverStateRenderer()
    self._confidence_ball = ConfidenceBall()
+    self._milestone_celebration = self._child(MilestoneCelebration()) if MILESTONE_CELEBRATION_ENABLED else None
    self._offroad_label = UnifiedLabel("start the car to\nuse sunnypilot", 54, FontWeight.DISPLAY,
                                       text_color=rl.Color(255, 255, 255, int(255 * 0.9)),
-                                       alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                       alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                                       alignment=TextAlignment.CENTER,
+                                       alignment_vertical=TextAlignmentVertical.MIDDLE)

    self._fade_texture = gui_app.texture("icons_mici/onroad/onroad_fade.png")

@@ -223,6 +229,12 @@ class AugmentedRoadView(CameraView):

    alert_to_render, not_animating_out = self._alert_renderer.will_render()

+    if self._milestone_celebration is not None:
+      if alert_to_render is not None:
+        self._milestone_celebration.cancel_for_alert()
+      else:
+        self._milestone_celebration.render(self._content_rect)
+
    # Hide DMoji when disengaged unless AlwaysOnDM is enabled
    should_draw_dmoji = (not self._hud_renderer.drawing_top_icons() and
                         (ui_state.status != UIStatus.DISENGAGED or ui_state.always_on_dm))
@@ -247,7 +259,6 @@ class AugmentedRoadView(CameraView):
    self._confidence_ball.render(self.rect)

    self._bookmark_icon.render(self.rect)
-
  def _switch_stream_if_needed(self, sm):
    if sm['selfdriveState'].experimentalMode and WIDE_CAM in self.available_streams:
      v_ego = sm['carState'].vEgo
@@ -355,10 +366,12 @@ class AugmentedRoadView(CameraView):
    return self._cached_matrix

  def show_event(self):
+    super().show_event()
    if gui_app.sunnypilot_ui():
      ui_state.reset_onroad_sleep_timer(OnroadTimerStatus.RESUME)

  def hide_event(self):
+    super().hide_event()
    if gui_app.sunnypilot_ui():
      ui_state.reset_onroad_sleep_timer(OnroadTimerStatus.PAUSE)

@@ -4,7 +4,7 @@ from openpilot.cereal.visionipc import VisionStreamType
 from openpilot.selfdrive.ui.mici.onroad.cameraview import CameraView
 from openpilot.selfdrive.ui.mici.onroad.driver_state import DriverStateRenderer
 from openpilot.selfdrive.ui.ui_state import ui_state, device
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.nav_widget import NavWidget
@@ -76,7 +76,7 @@ class BaseCabinCameraDialog(Widget):

    if not self._camera_view.frame:
      gui_label(rect, tr("camera starting"), font_size=54, font_weight=FontWeight.BOLD,
-                alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER)
+                alignment=TextAlignment.CENTER)
      rl.end_scissor_mode()
      self._publish_alert_sound(None)
      return
@@ -124,12 +124,12 @@ class BaseCabinCameraDialog(Widget):
    awareness_pct = dm_state.visionPolicyState.awarenessPercent if is_vision else dm_state.wheeltouchPolicyState.awarenessPercent
    gui_label(rl.Rectangle(rect.x + 2, rect.y + 2, rect.width, rect.height),
              f"Awareness: {awareness_pct:.0f}%", font_size=44, font_weight=FontWeight.MEDIUM,
-              alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+              alignment=TextAlignment.RIGHT,
+              alignment_vertical=TextAlignmentVertical.TOP,
              color=rl.Color(0, 0, 0, 180))
    gui_label(rect, f"Awareness: {awareness_pct:.0f}%", font_size=44, font_weight=FontWeight.MEDIUM,
-              alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+              alignment=TextAlignment.RIGHT,
+              alignment_vertical=TextAlignmentVertical.TOP,
              color=rl.Color(255, 255, 255, int(255 * 0.9)))

    if dm_state.alertLevel == log.DriverMonitoringState.AlertLevel.none:
@@ -137,16 +137,16 @@ class BaseCabinCameraDialog(Widget):

    # Show alert level
    alert_level_str = f"{'Pay Attention' if is_vision else 'Touch Wheel'} - level {dm_state.alertLevel}"
-    alignment = rl.GuiTextAlignment.TEXT_ALIGN_RIGHT if self.driver_state_renderer.is_rhd else rl.GuiTextAlignment.TEXT_ALIGN_LEFT
+    alignment = TextAlignment.RIGHT if self.driver_state_renderer.is_rhd else TextAlignment.LEFT

    shadow_rect = rl.Rectangle(rect.x + 2, rect.y + 2, rect.width, rect.height)
    gui_label(shadow_rect, alert_level_str, font_size=40, font_weight=FontWeight.BOLD,
              alignment=alignment,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM,
+              alignment_vertical=TextAlignmentVertical.BOTTOM,
              color=rl.Color(0, 0, 0, 180))
    gui_label(rect, alert_level_str, font_size=40, font_weight=FontWeight.BOLD,
              alignment=alignment,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM,
+              alignment_vertical=TextAlignmentVertical.BOTTOM,
              color=rl.Color(255, 255, 255, int(255 * 0.9)))

  def _load_eye_textures(self):
@@ -3,7 +3,7 @@ import pyray as rl
 from dataclasses import dataclass
 from openpilot.common.constants import CV
 from openpilot.selfdrive.ui.mici.onroad.torque_bar import TorqueBar
-from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
+from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus, ChestnutState
 from openpilot.system.ui.lib.application import gui_app, FontWeight
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.lib.text_measure import measure_text_cached
@@ -107,8 +107,7 @@ class HudRenderer(Widget):
    self.speed: float = 0.0
    self.v_ego_cluster_seen: bool = False
    self._engaged: bool = False
-    self._small_model_engaged: bool = False
-    self._egpu_fade_time: float = 0
+    self._chestnut_fade_time: float = 0

    self._can_draw_top_icons = True
    self._show_wheel_critical = False
@@ -124,17 +123,15 @@ class HudRenderer(Widget):
    self._txt_wheel: rl.Texture = gui_app.texture('icons_mici/wheel.png', 50, 50)
    self._txt_wheel_critical: rl.Texture = gui_app.texture('icons_mici/wheel_critical.png', 50, 50)
    self._txt_exclamation_point: rl.Texture = gui_app.texture('icons_mici/exclamation_point.png', 9, 44)
-    self._txt_egpu: rl.Texture = gui_app.texture('icons_mici/egpu.png', 60, 44)
-    self._txt_egpu_green: rl.Texture = gui_app.texture('icons_mici/egpu_green.png', 60, 44)
-    self._txt_egpu_orange: rl.Texture = gui_app.texture('icons_mici/egpu_orange.png', 60, 44)
-    self._txt_egpu_crossed: rl.Texture = gui_app.texture('icons_mici/egpu_crossed.png', 60, 52)
-    self._egpu_icon: rl.Texture | None = None
-
+    self._txt_chestnut: rl.Texture = gui_app.texture('icons_mici/chestnut.png', 60, 44)
+    self._txt_chestnut_green: rl.Texture = gui_app.texture('icons_mici/chestnut_green.png', 60, 44)
+    self._txt_chestnut_orange: rl.Texture = gui_app.texture('icons_mici/chestnut_orange.png', 75, 44)
+    self._chestnut_icon: rl.Texture | None = None
    self._wheel_alpha_filter = FirstOrderFilter(0, 0.05, 1 / gui_app.target_fps)
    self._wheel_y_filter = FirstOrderFilter(0, 0.1, 1 / gui_app.target_fps)

    self._set_speed_alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)
-    self._egpu_alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)
+    self._chestnut_alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)

  def set_wheel_critical_icon(self, critical: bool):
    """Set the wheel icon to critical or normal state."""
@@ -165,13 +162,10 @@ class HudRenderer(Widget):
      controls_state.deprecated.vCruise if v_cruise_cluster == 0.0 else v_cruise_cluster
    )
    engaged = sm['selfdriveState'].enabled
-    if (engaged and not self._engaged and not ui_state.usbgpu_loading and ui_state.usbgpu_active is not True and
-        ui_state.sm.recv_frame['modelV2'] > ui_state.started_frame):
-      self._small_model_engaged = True
-    if engaged != self._engaged:
-      self._egpu_fade_time = rl.get_time() if engaged else 0
    if (set_speed != self.set_speed and engaged) or (engaged and not self._engaged):
      self._set_speed_changed_time = rl.get_time()
+    if engaged != self._engaged:
+      self._chestnut_fade_time = rl.get_time() if engaged else 0
    self._engaged = engaged
    self.set_speed = set_speed
    self.is_cruise_set = 0 < self.set_speed < SET_SPEED_NA
@@ -191,8 +185,7 @@ class HudRenderer(Widget):
    if self.is_cruise_set:
      self._draw_set_speed(rect)

-    if ui_state.usbgpu and ui_state.usbgpu_compiled:
-      self._draw_model_source(rect)
+    self._draw_model_source(rect)

    self._draw_steering_wheel(rect)

@@ -200,30 +193,24 @@ class HudRenderer(Widget):
    if ui_state.sm.recv_frame['selfdriveState'] < ui_state.started_frame:
      return

-    big_failed = (ui_state.usbgpu_active is False or not ui_state.sm['deviceState'].chestnutPresent or
-                  (ui_state.usbgpu_active is True and ui_state.sm.recv_frame['modelV2'] > ui_state.started_frame and
-                   not ui_state.sm.alive['modelV2']) or
-                  (ui_state.usbgpu_active is None and ui_state.sm.recv_frame['modelV2'] > ui_state.started_frame))
-    self._small_model_engaged &= big_failed
-    loading = ui_state.usbgpu_loading or (ui_state.usbgpu_active is None and not big_failed)
+    loading = ui_state.chestnut_state == ChestnutState.LOADING
    if loading:
-      pulse = 0.5 - 0.5 * math.cos(rl.get_time() * 6.0)
-      icon = self._txt_egpu
-      opacity = 0.35 + 0.65 * pulse
-    elif self._small_model_engaged:
-      icon = self._txt_egpu_crossed
-      opacity = 0.65
-    elif big_failed:
-      icon = self._txt_egpu_orange
+      icon = self._txt_chestnut
+      opacity = 0.35 + 0.65 * (0.5 - 0.5 * math.cos(rl.get_time() * 6.0))
+    elif ui_state.chestnut_state in (ChestnutState.UNCOMPILED, ChestnutState.FAILED):
+      icon = self._txt_chestnut_orange
+      opacity = 1.0
+    elif ui_state.chestnut_state == ChestnutState.ACTIVE:
+      icon = self._txt_chestnut_green
      opacity = 1.0
    else:
-      icon = self._txt_egpu_green
-      opacity = 1.0
+      return

-    if icon is not self._egpu_icon:
-      self._egpu_fade_time = rl.get_time()
-      self._egpu_icon = icon
-    alpha = self._egpu_alpha_filter.update(loading or 0 < rl.get_time() - self._egpu_fade_time < SET_SPEED_PERSISTENCE)
+    if icon is not self._chestnut_icon:
+      self._chestnut_fade_time = rl.get_time()
+      self._chestnut_icon = icon
+    visible = loading or rl.get_time() - self._chestnut_fade_time < SET_SPEED_PERSISTENCE
+    alpha = self._chestnut_alpha_filter.update(visible)
    if alpha < 1e-2:
      return

@@ -6,7 +6,7 @@ from collections.abc import Callable
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets.scroller import DO_ZOOM
-from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos
+from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos, TextAlignmentVertical
 from openpilot.common.filter_simple import BounceFilter

 if TYPE_CHECKING:
@@ -125,10 +125,10 @@ class BigButton(Widget):
    self._rotate_icon_t: float | None = None

    self._label = UnifiedLabel(text, font_size=self._get_label_font_size(), font_weight=FontWeight.BOLD,
-                               text_color=LABEL_COLOR, alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM, scroll=scroll,
+                               text_color=LABEL_COLOR, alignment_vertical=TextAlignmentVertical.BOTTOM, scroll=scroll,
                               line_height=0.9)
    self._sub_label = UnifiedLabel(value, font_size=COMPLICATION_SIZE, font_weight=FontWeight.ROMAN,
-                                   text_color=COMPLICATION_GREY, alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM)
+                                   text_color=COMPLICATION_GREY, alignment_vertical=TextAlignmentVertical.BOTTOM)
    self._update_label_layout()

    self._load_images()
@@ -149,11 +149,15 @@ class BigButton(Widget):
  def set_touch_valid_callback(self, touch_callback: Callable[[], bool]) -> None:
    super().set_touch_valid_callback(lambda: touch_callback() and self._grow_animation_until is None)

-  def _width_hint(self) -> int:
-    # A value moves the title to the top, where it shares space with the icon.
+  def _title_width_hint(self) -> int:
+    # A value moves the title to the top, where it shares space with the icon
    icon_size = self._txt_icon.width if self._txt_icon and self.value else 0
    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2 - icon_size)

+  def _subtitle_width_hint(self) -> int:
+    # Bottom aligned, so it sits below the icon
+    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2)
+
  def _get_label_font_size(self):
    if len(self.text) <= 18:
      return 48
@@ -163,9 +167,9 @@ class BigButton(Widget):
  def _update_label_layout(self):
    self._label.set_font_size(self._get_label_font_size())
    if self.value:
-      self._label.set_alignment_vertical(rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP)
+      self._label.set_alignment_vertical(TextAlignmentVertical.TOP)
    else:
-      self._label.set_alignment_vertical(rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM)
+      self._label.set_alignment_vertical(TextAlignmentVertical.BOTTOM)

  def set_text(self, text: str):
    self.text = text
@@ -228,14 +232,14 @@ class BigButton(Widget):

    label_color = LABEL_COLOR if self.enabled else rl.Color(255, 255, 255, int(255 * 0.35))
    self._label.set_color(label_color)
-    label_rect = rl.Rectangle(label_x, btn_y + self.LABEL_VERTICAL_PADDING, self._width_hint(),
+    label_rect = rl.Rectangle(label_x, btn_y + self.LABEL_VERTICAL_PADDING, self._title_width_hint(),
                              self._rect.height - self.LABEL_VERTICAL_PADDING * 2)
    self._label.render(label_rect)

    if self.value:
-      label_y = btn_y + self.LABEL_VERTICAL_PADDING + self._label.get_content_height(self._width_hint())
+      label_y = label_rect.y + self._label.get_content_height(int(label_rect.width))
      sub_label_height = btn_y + self._rect.height - self.LABEL_VERTICAL_PADDING - label_y
-      sub_label_rect = rl.Rectangle(label_x, label_y, self._width_hint(), sub_label_height)
+      sub_label_rect = rl.Rectangle(label_x, label_y, self._subtitle_width_hint(), sub_label_height)
      self._sub_label.render(sub_label_rect)

    # ICON -------------------------------------------------------------------
@@ -312,9 +316,6 @@ class BigMultiToggle(BigToggle):

    self.set_value(self._options[0])

-  def _width_hint(self) -> int:
-    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2 - self._txt_enabled_toggle.width)
-
  def _handle_mouse_release(self, mouse_pos: MousePos):
    super()._handle_mouse_release(mouse_pos)
    cur_idx = self._options.index(self.value)
@@ -355,17 +356,14 @@ class GreyBigButton(BigButton):
    self._sub_label.set_font_size(36)
    self._sub_label.set_text_color(rl.Color(255, 255, 255, int(255 * 0.9)))
    self._sub_label.set_font_weight(FontWeight.DISPLAY_REGULAR)
-    self._sub_label.set_alignment_vertical(rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE if not self._label.text else
-                                           rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM)
+    self._sub_label.set_alignment_vertical(TextAlignmentVertical.MIDDLE if not self._label.text else
+                                           TextAlignmentVertical.BOTTOM)
    self._sub_label.set_line_height(0.95)

  @property
  def LABEL_VERTICAL_PADDING(self):
    return BigButton.LABEL_VERTICAL_PADDING if self._label.text else 18

-  def _width_hint(self) -> int:
-    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2)
-
  def _get_label_font_size(self):
    return 36

@@ -4,7 +4,7 @@ from dataclasses import dataclass
 from openpilot.cereal import messaging, log
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.common.hardware import COMMA_HARDWARE
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.lib.text_measure import measure_text_cached
 from openpilot.system.ui.widgets import Widget
@@ -76,10 +76,10 @@ class AlertRenderer(Widget):
    self.font_bold: rl.Font = gui_app.font(FontWeight.BOLD)

    # font size is set dynamically
-    self._full_text1_label = Label("", font_size=0, font_weight=FontWeight.BOLD, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                   text_alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP)
-    self._full_text2_label = Label("", font_size=ALERT_FONT_BIG, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                   text_alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP)
+    self._full_text1_label = Label("", font_size=0, font_weight=FontWeight.BOLD, text_alignment=TextAlignment.CENTER,
+                                   text_alignment_vertical=TextAlignmentVertical.TOP)
+    self._full_text2_label = Label("", font_size=ALERT_FONT_BIG, text_alignment=TextAlignment.CENTER,
+                                   text_alignment_vertical=TextAlignmentVertical.TOP)

  def get_alert(self, sm: messaging.SubMaster) -> Alert | None:
    """Generate the current alert based on selfdrive state."""
@@ -4,7 +4,7 @@ from openpilot.cereal.visionipc import VisionStreamType
 from openpilot.selfdrive.ui.onroad.cameraview import CameraView
 from openpilot.selfdrive.ui.onroad.driver_state import DriverStateRenderer
 from openpilot.selfdrive.ui.ui_state import ui_state, device
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets.label import gui_label

@@ -38,7 +38,7 @@ class CabinCameraDialog(CameraView):
        tr("camera starting"),
        font_size=100,
        font_weight=FontWeight.BOLD,
-        alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
+        alignment=TextAlignment.CENTER,
      )
      return -1

@@ -24,14 +24,8 @@ ALERT_RAMP_TIME = 4 # seconds to ramp to max volume for warningImmediate
 SELFDRIVE_STATE_TIMEOUT = 5 # 5 seconds
 FILTER_DT = 1. / (micd.SAMPLE_RATE / micd.FFT_SAMPLES)

-AMBIENT_DB = 26 # DB where MIN_VOLUME is applied
 DB_SCALE = 30 # AMBIENT_DB + DB_SCALE is where MAX_VOLUME is applied

-VOLUME_BASE = 20
-if HARDWARE.get_device_type() == "tizi":
-  AMBIENT_DB = 30
-  VOLUME_BASE = 10
-
 AudibleAlert = log.SelfdriveState.AudibleAlert
 AudibleAlertSP = custom.SelfdriveStateSP.AudibleAlert

@@ -53,6 +47,7 @@ sound_list: dict[int, tuple[str, int | None, float]] = {
  AudibleAlert.promptDistracted: ("dm_warning.wav", None, MAX_VOLUME),

  AudibleAlert.preAlert: ("pre_alert.wav", 1, MAX_VOLUME),
+  AudibleAlert.complete: ("milestone.wav", 1, MAX_VOLUME),

  AudibleAlert.warningSoft: ("critical.wav", None, MAX_VOLUME),
  AudibleAlert.warningImmediate: ("dm_critical.wav", None, MAX_VOLUME),
@@ -60,6 +55,14 @@ sound_list: dict[int, tuple[str, int | None, float]] = {
  **sound_list_sp,
 }

+
+def calculate_volume_for_device(weighted_db: float, device_type: str) -> float:
+  ambient_db = 30 if device_type in ("mici", "tizi") else 26
+  volume_base = 10 if device_type in ("mici", "tizi") else 20
+  volume_boost = 1.5 if device_type == "mici" else 1.0
+  volume = ((weighted_db - ambient_db) / DB_SCALE) * (MAX_VOLUME - MIN_VOLUME) + MIN_VOLUME
+  return min(MAX_VOLUME, volume_boost * math.pow(volume_base, (np.clip(volume, MIN_VOLUME, MAX_VOLUME) - 1)))
+
 def check_selfdrive_timeout_alert(sm):
  ss_missing = time.monotonic() - sm.recv_time['selfdriveState']

@@ -74,6 +77,7 @@ class Soundd(QuietMode):
  def __init__(self):
    super().__init__()

+    self.device_type = HARDWARE.get_device_type()
    self.load_sounds()

    self.current_alert = AudibleAlert.none
@@ -85,6 +89,7 @@ class Soundd(QuietMode):

    self.selfdrive_timeout_alert = False
    self.pending_stop = False
+    self.last_milestone_event_id = 0

    self.spl_filter_weighted = FirstOrderFilter(0, 2.5, FILTER_DT, initialized=False)

@@ -164,9 +169,19 @@ class Soundd(QuietMode):
      self.update_alert(AudibleAlert.none)
      self.selfdrive_timeout_alert = False

+  def update_milestone_alert(self, sm):
+    if not sm.updated['assistedDrivingMilestoneState']:
+      return
+    milestone_state = sm['assistedDrivingMilestoneState']
+    event_id = milestone_state.event.id
+    if not milestone_state.enabled or event_id == 0 or event_id == self.last_milestone_event_id:
+      return
+    self.last_milestone_event_id = event_id
+    if self.current_alert == AudibleAlert.none and not self.enabled:
+      self.update_alert(AudibleAlert.complete)
+
  def calculate_volume(self, weighted_db):
-    volume = ((weighted_db - AMBIENT_DB) / DB_SCALE) * (MAX_VOLUME - MIN_VOLUME) + MIN_VOLUME
-    return math.pow(VOLUME_BASE, (np.clip(volume, MIN_VOLUME, MAX_VOLUME) - 1))
+    return calculate_volume_for_device(weighted_db, self.device_type)

  @retry(attempts=10, delay=3)
  def get_stream(self, sd):
@@ -180,7 +195,7 @@ class Soundd(QuietMode):
    import sounddevice as sd
    micd.patch_sounddevice(sd)

-    sm = messaging.SubMaster(['selfdriveState', 'selfdriveStateSP', 'soundPressure'])
+    sm = messaging.SubMaster(['selfdriveState', 'selfdriveStateSP', 'soundPressure', 'assistedDrivingMilestoneState'])

    with self.get_stream(sd) as stream:
      rk = Ratekeeper(20)
@@ -198,6 +213,7 @@ class Soundd(QuietMode):
            self.current_volume = self.calculate_volume(float(self.spl_filter_weighted.x))

        self.get_audible_alert(sm)
+        self.update_milestone_alert(sm)

        # Ramp up immediate warning sound over 4s
        if self.current_alert == AudibleAlert.warningImmediate:
@@ -6,7 +6,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 import pyray as rl
 from openpilot.selfdrive.ui.layouts.home import HomeLayout, HomeLayoutState, HEAD_BUTTON_FONT_SIZE, SPACING
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment
 from openpilot.system.ui.lib.text_measure import measure_text_cached
 from openpilot.system.ui.lib.multilang import tr, trn
 from openpilot.system.ui.widgets.label import gui_label
@@ -59,7 +59,7 @@ class HomeLayoutSP(HomeLayout):
      desc_size = measure_text_cached(gui_app.font(FontWeight.NORMAL), description, BRAND_FONT_SIZE)
      desc_width = desc_size.x
      desc_rect = rl.Rectangle(version_right - desc_width, self.header_rect.y, desc_width, self.header_rect.height)
-      gui_label(desc_rect, description, BRAND_FONT_SIZE, rl.WHITE, alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+      gui_label(desc_rect, description, BRAND_FONT_SIZE, rl.WHITE, alignment=TextAlignment.RIGHT)

    brand_size = measure_text_cached(gui_app.font(FontWeight.AUDIOWIDE), brand, BRAND_FONT_SIZE)
    spacing = BRAND_DESC_SPACING if description else 0
@@ -6,7 +6,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 import pyray as rl
 from openpilot.selfdrive.ui.ui_state import ui_state
-from openpilot.system.ui.lib.application import FontWeight
+from openpilot.system.ui.lib.application import FontWeight, TextAlignment
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.button import Button, ButtonStyle
@@ -20,7 +20,7 @@ class SunnylinkConsentPage(Widget):
    self._done_callback = done_callback
    self._step = 0

-    self._title = self._child(Label(tr("sunnylink"), font_size=90, font_weight=FontWeight.AUDIOWIDE, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT))
+    self._title = self._child(Label(tr("sunnylink"), font_size=90, font_weight=FontWeight.AUDIOWIDE, text_alignment=TextAlignment.LEFT))

    self._content = [
      {
@@ -43,7 +43,7 @@ class SunnylinkConsentPage(Widget):
    self._primary_btn = self._child(Button("", button_style=ButtonStyle.PRIMARY, click_callback=lambda: self._handle_choice("enable")))
    self._secondary_btn = self._child(Button("", button_style=ButtonStyle.NORMAL, click_callback=lambda: self._handle_choice("secondary")))
    self._danger_btn = self._child(Button("", button_style=ButtonStyle.DANGER, click_callback=lambda: self._handle_choice("disable")))
-    self._desc = self._child(Label("", font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT))
+    self._desc = self._child(Label("", font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=TextAlignment.LEFT))

  def _handle_choice(self, choice):
    if choice == "enable":
@@ -10,9 +10,10 @@ import time
 import pyray as rl

 from openpilot.cereal import custom
-from openpilot.sunnypilot.models.default_model import get_default_model
+from openpilot.sunnypilot.models.helpers import ACTIVE_BUNDLE_KEYS, get_selected_bundle, resolve_bundle_by_ref
 from openpilot.common.constants import CV
 from openpilot.selfdrive.ui.ui_state import device, ui_state
+from openpilot.selfdrive.ui.sunnypilot.model_info import big_model_state, bundles_for_source, carrying_model, default_model_name, queued_name
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.lib.application import gui_app
 from openpilot.system.ui.widgets import DialogResult, Widget
@@ -36,7 +37,10 @@ class ModelsLayout(Widget):
    super().__init__()
    self.model_manager = None
    self.model_dialog = None
+    self._selection_source = None
    self._downloading = False
+    self._verifying = False
+    self._last_note = None
    self.last_cache_calc_time = 0

    self._initialize_items()
@@ -48,17 +52,24 @@ class ModelsLayout(Widget):
    self._scroller = Scroller(self.items, line_separator=True, spacing=0)

  def _initialize_items(self):
-    self.current_model_item = ListItemSP(
-      title=tr("Current Model"),
+    self.small_model_item = ListItemSP(
+      title=tr("Small Model"),
      description="",
      action_item=ScrollingButtonAction(tr("SELECT")),
-      callback=self._handle_current_model_clicked
+      callback=lambda: self._open_source_dialog("qcom")
+    )
+
+    self.big_model_item = ListItemSP(
+      title=tr("Big Model"),
+      action_item=ScrollingButtonAction(tr("SELECT")),
+      callback=lambda: self._open_source_dialog("chestnut")
    )

    self.download_item = download_status_item(lambda: tr("Download") if self._downloading else tr("Model Status"))

    self.refresh_item = button_item(tr("Refresh Model List"), tr("REFRESH"), "",
                                    lambda: (ui_state.params.put("ModelManager_LastSyncTime", 0),
+                                             ui_state.params.put("ModelManager_LastSyncTime_Chestnut", 0),
                                             gui_app.push_widget(alert_dialog(tr("Fetching Latest Models")))))

    self.clear_cache_item = ListItemSP(
@@ -68,7 +79,9 @@ class ModelsLayout(Widget):
      callback=self._clear_cache
    )

-    self.cancel_download_item = button_item(tr("Cancel Download"), tr("Cancel"), "", lambda: ui_state.params.remove("ModelManager_DownloadIndex"))
+    self.cancel_download_item = button_item(lambda: tr("Cancel Verification") if self._verifying else tr("Cancel Download"),
+                                            tr("Cancel"), "",
+                                            lambda: ui_state.params.remove("ModelManager_DownloadRef"))

    self.lane_turn_value_control = option_item_sp(tr("Adjust Lane Turn Speed"), "LaneTurnValue", 500, 2000,
                                                  tr("Set the maximum speed for lane turn desires. Default is 19 mph."),
@@ -93,7 +106,7 @@ class ModelsLayout(Widget):
                                        1, None, True, "", style.BUTTON_ACTION_WIDTH, None, True,
                                        lambda v: f"{v / 100:.2f} m")

-    self.items = [self.current_model_item, self.cancel_download_item, self.download_item, self.refresh_item, self.clear_cache_item,
+    self.items = [self.small_model_item, self.big_model_item, self.cancel_download_item, self.download_item, self.refresh_item, self.clear_cache_item,
                  self.lane_turn_desire_toggle, self.lane_turn_value_control, self.lagd_toggle, self.delay_control, self.camera_offset]

  def _update_lagd_description(self, lagd_toggle: bool):
@@ -107,16 +120,16 @@ class ModelsLayout(Widget):
      desc += f"<br>{tr('Actuator Delay:')} {cp:.2f} s + {tr('Software Delay:')} {sw:.2f} s = {tr('Total Delay:')} {cp + sw:.2f} s"
    self.lagd_toggle.set_description(desc)

-  def _is_downloading(self):
-    return (self.model_manager and self.model_manager.selectedBundle and
-            self.model_manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.downloading)
-
  @staticmethod
  def calculate_cache_size():
    cache_size = 0.0
    if os.path.exists(CUSTOM_MODEL_PATH):
-      cache_size = sum(os.path.getsize(os.path.join(CUSTOM_MODEL_PATH, file)) for file in os.listdir(CUSTOM_MODEL_PATH)) / (1024**2)
-    return cache_size
+      for file in os.listdir(CUSTOM_MODEL_PATH):
+        try:
+          cache_size += os.path.getsize(os.path.join(CUSTOM_MODEL_PATH, file))
+        except OSError:
+          continue
+    return cache_size / (1024**2)

  def _clear_cache(self):
    def _callback(response):
@@ -129,36 +142,90 @@ class ModelsLayout(Widget):
    gui_app.push_widget(dialog)

  def _handle_bundle_download_progress(self):
-    self.download_item.set_visible(False)
    self.cancel_download_item.set_visible(False)
    self._downloading = False
-
-    if not self.model_manager or (not self.model_manager.selectedBundle and not self.model_manager.activeBundle):
-      return
-
-    bundle = self.model_manager.selectedBundle if self._is_downloading() or (
-      self.model_manager.selectedBundle and self.model_manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.failed
-    ) else self.model_manager.activeBundle
-    if not bundle:
-      return
-
-    self.cancel_download_item.set_visible(bool(self.model_manager.selectedBundle) and ui_state.params.get("ModelManager_DownloadIndex") is not None)
+    self._verifying = False
+    self.download_item.set_visible(True)

    if (current_time := time.monotonic()) - self.last_cache_calc_time > 0.5:
      self.last_cache_calc_time = current_time
      self.clear_cache_item.action_item.set_value(f"{self.calculate_cache_size():.2f} MB")

+    bundle = self.model_manager.selectedBundle if self.model_manager else None
+    progresses = [model.artifact.downloadProgress for model in bundle.models if model.artifact.fileName] if bundle else []
+    if not progresses or bundle.status not in (custom.ModelManagerSP.DownloadStatus.downloading,
+                                               custom.ModelManagerSP.DownloadStatus.failed):
+      self.download_item.action_item.update(name="", segments=self._slot_segments())
+      return
+
+    self.cancel_download_item.set_visible(ui_state.params.get("ModelManager_DownloadRef") is not None)
    if bundle.status == custom.ModelManagerSP.DownloadStatus.downloading:
      device._reset_interactive_timeout()

-    # every bundle is a single chunked artifact now
-    progresses = [model.artifact.downloadProgress for model in bundle.models if model.artifact.fileName]
-    if not progresses:
-      return
-
-    self.download_item.set_visible(True)
-    self.download_item.action_item.update(**self._download_row_state(progresses, bundle.internalName))
+    state = self._download_row_state(progresses, bundle.internalName)
+    if queued := queued_name(bundle.ref):
+      state["name"] += f"  |  {queued} {tr('queued')}"
+    self.download_item.action_item.update(**state)
    self._downloading = self.download_item.action_item.downloading
+    ds = custom.ModelManagerSP.DownloadStatus
+    self._verifying = any(getattr(p.status, 'raw', p.status) == ds.verifying for p in progresses)
+
+  def _slot_segments(self):
+    """small and big slots side by side; green marks the slot whose pick is actually
+    driving (runner-matched, so a failed Default big greens neither slot), an empty
+    slot shows its default."""
+    big_state = big_model_state()
+    carry_source, carry_internal, _ = carrying_model()
+    segments = []
+    for source, label in (("qcom", tr("small")), ("chestnut", tr("big"))):
+      if segments:
+        segments.append(("|", rl.GRAY, None, None))
+      bundle = get_selected_bundle(ui_state.params, source)
+      name = bundle.internalName if bundle else default_model_name(source)
+      color = ON_COLOR if (source == carry_source and name == carry_internal) else rl.LIGHTGRAY
+      name = "● " + name
+      if source == "chestnut":
+        if big_state == 'failed':
+          color = rl.RED
+        elif big_state == 'loading':
+          color = rl.GOLD
+      segments.append((label, rl.GRAY, None, None))
+      segments.append((name, color, None, None))
+    return segments
+
+  @staticmethod
+  def _set_item_note(item, text):
+    # a description renders only while shown; hide before clearing or the
+    # empty description keeps its visible state
+    if text:
+      item.set_description(text)
+      item.show_description(True)
+    else:
+      item.show_description(False)
+      item.set_description("")
+
+  def _status_note(self) -> str:
+    """The failover story for the Model Status row. One-way big -> small, and the
+    fallback is runner-matched: a Default big can only fall back to the Default
+    small (stock modeld), a custom big has no automatic fallback yet."""
+    if not ui_state.chestnut_present:
+      return ""
+    big_bundle = get_selected_bundle(ui_state.params, "chestnut")
+    big_name = big_bundle.internalName if big_bundle else default_model_name("chestnut")
+    big_is_default = big_bundle is None
+    fallback_name = default_model_name("qcom")
+    state = big_model_state()
+    if state == 'failed':
+      if big_is_default:
+        return tr("Big model unavailable, {} is driving until the next drive.").format(fallback_name)
+      return tr("Big model unavailable until the next drive.")
+    if state == 'loading':
+      if big_is_default:
+        return tr("{} drives until the big model is ready.").format(fallback_name)
+      return tr("Getting the big model ready.")
+    if big_is_default:
+      return tr("{} will drive. If it fails during a drive, {} takes over until the next drive.").format(big_name, fallback_name)
+    return tr("{} will drive when the chestnut is ready.").format(big_name)

  @staticmethod
  def _download_row_state(progresses, name: str) -> dict:
@@ -171,6 +238,8 @@ class ModelsLayout(Widget):
    if ds.failed in statuses:
      # close.png is authored black and a tint cannot lift it, hence close2
      return {"name": name, "status_text": tr("download failed"), "text_color": rl.RED, "icon": "icons/close2.png"}
+    if ds.verifying in statuses:
+      return {"name": name, "downloading": True, "progress": progress, "status_text": tr("verifying")}
    if ds.downloading in statuses:
      return {"name": name, "downloading": True, "progress": progress}
    if statuses <= {ds.downloaded, ds.cached}:
@@ -178,65 +247,73 @@ class ModelsLayout(Widget):
    # circled_slash is authored grey; tinting it again only darkens it
    return {"name": name, "text_color": rl.GRAY, "icon": "icons/circled_slash.png", "icon_color": rl.WHITE}

-  @staticmethod
-  def _show_reset_params_dialog():
-    def _callback(response):
-      if response == DialogResult.CONFIRM:
-        ui_state.params.remove("CalibrationParams")
-        ui_state.params.remove("LiveTorqueParameters")
-    msg = tr("Model download has started in the background. We suggest resetting calibration. Would you like to do that now?")
-    dialog = ConfirmDialog(msg, tr("Reset Calibration"), callback=_callback)
-    gui_app.push_widget(dialog)
-
  def _on_model_selected(self, result):
    if result != DialogResult.CONFIRM:
+      self.model_dialog = None
      return
    selected_ref = self.model_dialog.selection_ref
-    if selected_ref == "Default":
-      ui_state.params.remove("ModelManager_ActiveBundle")
-      self._show_reset_params_dialog()
-    elif selected_bundle := next((bundle for bundle in self.model_manager.availableBundles if bundle.ref == selected_ref), None):
-      ui_state.params.put("ModelManager_DownloadIndex", selected_bundle.index)
-      if self.model_manager.activeBundle and selected_bundle.generation != self.model_manager.activeBundle.generation:
-        self._show_reset_params_dialog()
    self.model_dialog = None
+    if selected_ref == "Default":
+      if self._selection_source in ACTIVE_BUNDLE_KEYS:
+        ui_state.params.remove(ACTIVE_BUNDLE_KEYS[self._selection_source])
+      return
+    if selected_bundle := self._resolve_selected_bundle(selected_ref):
+      ui_state.params.put("ModelManager_DownloadRef", selected_bundle.ref)
+
+  def _resolve_selected_bundle(self, ref):
+    source_bundles = {source: bundles_for_source(source) for source in ("qcom", "chestnut")}
+    resolved = resolve_bundle_by_ref(ref, source_bundles)
+    return resolved[0] if resolved else None

  @staticmethod
  def _bundle_to_node(bundle):
    return TreeNode(bundle.ref, {'display_name': bundle.displayName, 'short_name': bundle.internalName})

-  def _get_folders(self, favorites):
-    bundles = self.model_manager.availableBundles
+  def _get_folders(self, favorites, bundles):
    folders = {}
    for bundle in bundles:
      folders.setdefault(next((ov_ride.value for ov_ride in bundle.overrides if ov_ride.key == "folder"), ""), []).append(bundle)

-    folders_list = [TreeFolder("", [TreeNode("Default", {'display_name': f"{get_default_model()} (Default)",
-                                                         'short_name': "Default"})])]
+    folders_list = []
    for folder, folder_bundles in sorted(folders.items(), key=lambda x: max((bundle.index for bundle in x[1]), default=-1), reverse=True):
      folder_bundles.sort(key=lambda bundle: bundle.index, reverse=True)
      name = folder + (f" - (Updated: {m.group(1)})" if folder_bundles and (m := re.search(r'\(([^)]*)\)[^(]*$', folder_bundles[0].displayName)) else "")
      folders_list.append(TreeFolder(name, [self._bundle_to_node(bundle) for bundle in folder_bundles]))

    if favorites and (fav_bundles := [bundle for bundle in bundles if bundle.ref in favorites]):
-      folders_list.insert(1, TreeFolder("Favorites", [self._bundle_to_node(bundle) for bundle in fav_bundles]))
+      folders_list.insert(0, TreeFolder("Favorites", [self._bundle_to_node(bundle) for bundle in fav_bundles]))
    return folders_list

-  def _handle_current_model_clicked(self):
+  def _open_source_dialog(self, source):
+    self._selection_source = source
    favs = ui_state.params.get("ModelManager_Favs")
    favorites = set(favs.split(';')) if favs else set()
-    folders_list = self._get_folders(favorites)
-
-    active_ref = self.model_manager.activeBundle.ref if self.model_manager.activeBundle else "Default"
-    self.model_dialog = TreeOptionDialog(tr("Select a Model"), folders_list, active_ref, "ModelManager_Favs",
-                                         get_folders_fn=self._get_folders, on_exit=self._on_model_selected)
+    folders_list = self._source_folders(favorites, source)
+    if not folders_list:
+      gui_app.push_widget(alert_dialog(tr("No models are available for this hardware yet. Connect to the internet and refresh the model list.")))
+      return
+    self.model_dialog = TreeOptionDialog(tr("Select a Model"), folders_list, self._slot_active_ref(source), "ModelManager_Favs",
+                                         get_folders_fn=lambda favs: self._source_folders(favs, source), on_exit=self._on_model_selected)
    gui_app.push_widget(self.model_dialog)

+  def _source_folders(self, favorites, source):
+    bundles = bundles_for_source(source)
+    if not bundles:
+      return []
+    folders_list = [TreeFolder("", [TreeNode("Default", {'display_name': default_model_name(source)})])]
+    folders_list.extend(self._get_folders(favorites, bundles))
+    return folders_list
+
+  @staticmethod
+  def _slot_active_ref(source: str) -> str:
+    bundle = get_selected_bundle(ui_state.params, source)
+    return bundle.ref if bundle else "Default"
+
  def _update_state(self):
    advanced_controls: bool = ui_state.params.get_bool("ShowAdvancedControls")
    turn_desire: bool = ui_state.params.get_bool("LaneTurnDesire")
    live_delay: bool = ui_state.params.get_bool("LagdToggle")
-    camera_offset: bool = ui_state.params.get("ModelManager_ActiveBundle") is not None
+    camera_offset: bool = ui_state.active_bundle is not None

    self.lane_turn_desire_toggle.action_item.set_state(turn_desire)
    self.lane_turn_value_control.set_visible(turn_desire and advanced_controls)
@@ -250,19 +327,27 @@ class ModelsLayout(Widget):
    self._update_lagd_description(live_delay)
    self.model_manager = ui_state.sm["modelManagerSP"]
    self._handle_bundle_download_progress()
-    default_label = f"{get_default_model()} (Default)"
-    active_name = self.model_manager.activeBundle.displayName if self.model_manager and self.model_manager.activeBundle.ref else default_label
-    self.current_model_item.action_item.set_value(active_name)

-    if not ui_state.is_offroad():
-      self.current_model_item.action_item.set_enabled(False)
-      self.current_model_item.set_description(tr("Only available when vehicle is off, or always offroad mode is on"))
-    else:
-      self.current_model_item.action_item.set_enabled(True)
-      self.current_model_item.set_description("")
+    carry_source, _, carry_display = carrying_model()
+    for item, item_source in ((self.small_model_item, "qcom"), (self.big_model_item, "chestnut")):
+      bundle = get_selected_bundle(ui_state.params, item_source)
+      name = bundle.displayName if bundle else default_model_name(item_source)
+      color = ON_COLOR if (item_source == carry_source and name == carry_display) else style.ITEM_TEXT_VALUE_COLOR
+      item.action_item.set_value(name, color)
+
+    note = self._status_note()
+    if note != self._last_note:
+      self._last_note = note
+      self._set_item_note(self.download_item, note)
+
+    offroad = ui_state.is_offroad()
+    self.small_model_item.action_item.set_enabled(offroad)
+    self.big_model_item.action_item.set_enabled(offroad)
+    self.small_model_item.set_description("" if offroad else tr("Only available when vehicle is off, or always offroad mode is on"))

  def _render(self, rect):
    self._scroller.render(rect)

  def show_event(self):
    self._scroller.show_event()
+    self._last_note = None  # re-expand the failover note every time the page opens
@@ -8,7 +8,6 @@ import datetime
 import os
 import platform
 import requests
-import shutil
 import threading
 from pathlib import Path
 from time import monotonic
@@ -75,22 +74,12 @@ class OSMLayout(Widget):
  def _update_map_size(self):
    threading.Thread(target=self.calculate_size, daemon=True).start()

-  def _do_delete_maps(self):
-    if MAP_PATH.exists():
-      shutil.rmtree(MAP_PATH)
-
-    for param in ("OsmDownloadedDate", "OsmLocal", "OsmLocationName", "OsmLocationTitle", "OsmStateName", "OsmStateTitle"):
-      ui_state.params.remove(param)
-
+  def _on_confirm_delete_maps(self):
+    ui_state.params.put_bool("Mapd_ClearCache", True)
    self._delete_maps_btn.action_item.set_enabled(True)
    self._delete_maps_btn.action_item.set_text(tr("DELETE"))
    self._update_map_size()

-  def _on_confirm_delete_maps(self):
-    self._delete_maps_btn.action_item.set_enabled(False)
-    self._delete_maps_btn.action_item.set_text("DELETING...")
-    threading.Thread(target=self._do_delete_maps).start()
-
  def _delete_maps(self):
    self._show_confirm(tr("This will delete ALL downloaded maps\n\nAre you sure you want to delete all maps?"),
                       tr("Yes, delete all maps"), self._on_confirm_delete_maps)
--- a/Show More
+++ b/Show More