Merge remote-tracking branch 'origin/laneful-curves' into tempt

.github/workflows/build-single-tinygrad-model.yaml

@@ -117,6 +117,10 @@ on:
         required: false
         type: string
         default: 'sunnypilot/sunnypilot-models'
+      tinygrad_ref:
+        description: 'Full tinygrad commit sha to convert a precompiled upstream pkl with, when it differs from the upstream pin'
+        required: false
+        type: string
 env:
   RECOMPILED_DIR: recompiled${{ inputs.recompiled_dir }}
   JSON_FILE: docs/docs/driving_models_${{ inputs.target_hardware == 'chestnut' && 'chestnut_v' || 'v' }}${{ inputs.json_version }}.json
@@ -130,6 +134,7 @@ jobs:
       is_20hz: ${{ inputs.is_20hz }}
       artifact_suffix: ${{ inputs.artifact_suffix }}
       target_hardware: ${{ inputs.target_hardware }}
+      tinygrad_ref: ${{ inputs.tinygrad_ref }}
     secrets: inherit
 
   publish_model:

.github/workflows/sunnypilot-build-model.yaml

@@ -35,6 +35,10 @@ on:
         required: false
         type: string
         default: 'qcom'
+      tinygrad_ref:
+        description: 'Full tinygrad commit sha to convert a precompiled upstream pkl with, when it differs from the upstream pin'
+        required: false
+        type: string
   workflow_dispatch:
     inputs:
       upstream_branch:
@@ -58,6 +62,10 @@ on:
         options:
           - qcom
           - chestnut
+      tinygrad_ref:
+        description: 'Full tinygrad commit sha to convert a precompiled upstream pkl with, when it differs from the upstream pin'
+        required: false
+        type: string
         default: 'qcom'
 
 
@@ -224,11 +232,13 @@ jobs:
 
           if [ -f "${{ env.MODELS_DIR }}/upstream_pkl.txt" ]; then
             read -r TG_URL TG_SHA _ < "${{ env.MODELS_DIR }}/upstream_pkl.txt"
+            if [ -n "${{ inputs.tinygrad_ref }}" ]; then TG_SHA="${{ inputs.tinygrad_ref }}"; fi
             echo "Converting commaai's precompiled model with tinygrad $TG_SHA"
             UPSTREAM_TG="${{ runner.temp }}/upstream_tinygrad"
-            rm -rf "$UPSTREAM_TG" && git init -q "$UPSTREAM_TG" && git -C "$UPSTREAM_TG" fetch -q --depth 1 "$TG_URL" "$TG_SHA" && git -C "$UPSTREAM_TG" checkout -q FETCH_HEAD
+            rm -rf "$UPSTREAM_TG" && git init -q "$UPSTREAM_TG" && git -C "$UPSTREAM_TG" fetch -q --depth 1 "$TG_URL" "$TG_SHA" && git -C "$UPSTREAM_TG" checkout -q FETCH_HEAD \
+              || { echo "::error::could not fetch tinygrad $TG_SHA from $TG_URL (a full 40-character commit sha is required)"; exit 1; }
             CONVERTED="${{ runner.temp }}/big_driving_tinygrad_plain.pkl"
-            env DEV=USB+AMD PYTHONPATH="$UPSTREAM_TG:${{ github.workspace }}" taskset -c 7 python3 openpilot/sunnypilot/modeld_v2/convert_upstream_pkl.py \
+            env DEV=USB+AMD PYTHONPATH="$UPSTREAM_TG:${{ github.workspace }}" taskset -c 7 python3 -u -X faulthandler openpilot/sunnypilot/modeld_v2/convert_upstream_pkl.py \
               "${{ env.MODELS_DIR }}/big_driving_tinygrad.pkl" "$CONVERTED"
             mv "$CONVERTED" "$OUTPUT_PKL"
           elif NEW_ARCH_ONNX=$(find "${{ env.MODELS_DIR }}" -maxdepth 1 -name "${{ inputs.target_hardware == 'chestnut' && 'big_' || '' }}*driving*.onnx" -print -quit) &&

openpilot/cereal/custom.capnp

@@ -468,6 +468,13 @@ struct ModelDataV2SP @0xa1680744031fdb2d {
   laneTurnDirection @0 :TurnDirection;
   leftLaneChangeEdgeBlock @1 :Bool;
   rightLaneChangeEdgeBlock @2 :Bool;
+  blendfulCurvature @3 :Float32;
+  blendfulTrust @4 :Float32;
+  modelCurvature @5 :Float32;
+  blendfulGoingAround @6 :Bool;
+  blendfulLeftProb @7 :Float32;
+  blendfulRightProb @8 :Float32;
+  blendfulLaneWidth @9 :Float32;
 
   enum TurnDirection {
     none @0;

openpilot/cereal/services.py

@@ -94,7 +94,7 @@ _services: dict[str, tuple] = {
   "carControlSP": (True, 100., 10),
   "carStateSP": (True, 100., 10),
   "liveMapDataSP": (True, 1., 1),
-  "modelDataV2SP": (True, 20., None, QueueSize.BIG),
+  "modelDataV2SP": (True, 20., 10, QueueSize.BIG),
   "liveLocationKalman": (True, 20.),
 
   # debug

openpilot/common/params_keys.h

@@ -258,6 +258,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"LagdValueCache", {PERSISTENT, FLOAT, "0.2"}},
     {"LaneTurnDesire", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"LaneTurnValue", {PERSISTENT | BACKUP, FLOAT, "19.0"}},
+    {"Blendful", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"PlanplusControl", {PERSISTENT | BACKUP, FLOAT, "1.0"}},
 
     // mapd

openpilot/selfdrive/ui/mici/onroad/model_renderer.py

@@ -141,6 +141,7 @@ class ModelRenderer(Widget, ModelRendererSP):
     if ui_state.status != UIStatus.DISENGAGED:
       self._draw_lane_lines()
       self._draw_path()
+      self.blendful.draw(self._rect)
 
     if render_lead_indicator:
       self._update_leads(sm)
@@ -219,6 +220,7 @@ class ModelRenderer(Widget, ModelRendererSP):
     # Update road edges using raw points
     for road_edge in self._road_edges:
       road_edge.projected_points = self._map_line_to_polygon(road_edge.raw_points, line_width_factor, 0.0, max_idx)
+    self.blendful.update(self, max_idx)
 
     # Update path using raw points
     if lead and lead.present:

openpilot/selfdrive/ui/onroad/model_renderer.py

@@ -123,6 +123,8 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
     # Draw elements
     self._draw_lane_lines()
     self._draw_path()
+    if self._lateral_active:
+      self.blendful.draw(self._rect)
 
     if render_lead_indicator and radar_state:
       self._draw_lead_indicator()
@@ -172,6 +174,7 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
     # Update road edges using raw points
     for road_edge in self._road_edges:
       road_edge.projected_points = self._map_line_to_polygon(road_edge.raw_points, 0.025, 0.0, max_idx, max_distance)
+    self.blendful.update(self, max_idx, max_distance)
 
     # Update path using raw points
     if lead and lead.present:

openpilot/selfdrive/ui/sunnypilot/layouts/settings/models.py

@@ -102,13 +102,18 @@ class ModelsLayout(Widget):
 
     self.lagd_toggle = toggle_item_sp(tr("Live Learning Steer Delay"), "", param="LagdToggle")
 
+    self.blendful_toggle = toggle_item_sp(tr("Blendful"),
+                                          tr("Nudges the car toward the lane center when the lane lines are clear. Drives laneless when they are not."),
+                                          param="Blendful")
+
     self.camera_offset = option_item_sp(tr("Adjust Camera Offset"), "CameraOffset", -35, 35,
                                         tr("Virtually shift camera's perspective to move model's center to Left(+ values) or Right (- values)"),
                                         1, None, True, "", style.BUTTON_ACTION_WIDTH, None, True,
                                         lambda v: f"{v / 100:.2f} m")
 
     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]
+                  self.blendful_toggle, 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):
     desc = tr("Enable this for the car to learn and adapt its steering response time. Disable to use a fixed steering response time. " +
@@ -322,6 +327,7 @@ class ModelsLayout(Widget):
     self.lane_turn_desire_toggle.action_item.set_state(turn_desire)
     self.lane_turn_value_control.set_visible(turn_desire and advanced_controls)
     self.lagd_toggle.action_item.set_state(live_delay)
+    self.blendful_toggle.action_item.set_state(ui_state.params.get_bool("Blendful"))
     self.delay_control.set_visible(not live_delay and advanced_controls)
     new_step = int(round(100 / CV.MPH_TO_KPH)) if ui_state.is_metric else 100
     if self.lane_turn_value_control.action_item is not None and self.lane_turn_value_control.action_item.value_change_step != new_step:

openpilot/selfdrive/ui/sunnypilot/mici/layouts/models.py

@@ -11,7 +11,7 @@ import pyray as rl
 from openpilot.cereal import custom
 from openpilot.selfdrive.ui.mici.widgets.dialog import BigConfirmationDialog, BigDialog
 from openpilot.sunnypilot.models.helpers import ACTIVE_BUNDLE_KEYS, get_selected_bundle
-from openpilot.selfdrive.ui.mici.widgets.button import BigButton
+from openpilot.selfdrive.ui.mici.widgets.button import BigButton, BigParamControl
 from openpilot.selfdrive.ui.ui_state import ui_state, device
 from openpilot.selfdrive.ui.sunnypilot.model_info import (active_source, big_model_state, bundles_for_source, carrying_model,
                                                            default_model_name, model_cache_size_mb, model_info, queued_name,
@@ -95,7 +95,12 @@ class ModelsLayoutMici(NavScroller):
     self.clear_cache_btn.set_click_callback(self._confirm_clear_cache)
     self._cache_size_time = 0.0
 
-    self.main_items = [self.current_model_info, self.select_model_btn, self.cancel_download_btn, self.refresh_btn, self.clear_cache_btn]
+    self.blendful_toggle = BigParamControl("blendful", "Blendful",
+                                           description="Nudges the car toward the lane center when the lane lines are clear.\n" +
+                                                       "Drives laneless when they are not.")
+
+    self.main_items = [self.current_model_info, self.select_model_btn, self.cancel_download_btn, self.refresh_btn, self.clear_cache_btn,
+                       self.blendful_toggle]
     self._scroller.add_widgets(self.main_items)
 
   @property
@@ -205,6 +210,10 @@ class ModelsLayoutMici(NavScroller):
       device.set_override_interactive_timeout(None)
       self._was_downloading = False
 
+  def show_event(self):
+    super().show_event()
+    self.blendful_toggle.refresh()
+
   def _update_state(self):
     super()._update_state()
 

openpilot/selfdrive/ui/sunnypilot/mici/onroad/model_renderer.py

@@ -6,6 +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 UIStatus
+from openpilot.selfdrive.ui.sunnypilot.onroad.blendful import BlendfulRenderer
 from openpilot.selfdrive.ui.sunnypilot.onroad.rainbow_path import RainbowPath
 
 LANE_LINE_COLORS_SP = {
@@ -17,3 +18,4 @@ LANE_LINE_COLORS_SP = {
 class ModelRendererSP:
   def __init__(self):
     self.rainbow_path = RainbowPath()
+    self.blendful = BlendfulRenderer()

openpilot/selfdrive/ui/sunnypilot/onroad/blendful.py

@@ -0,0 +1,90 @@
+"""
+Copyright (c) 2021-, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import numpy as np
+import pyray as rl
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.ui.ui_state import ui_state
+from openpilot.sunnypilot.modeld_v2.blendful import LANE_WIDTH_CLIP, LOOKAHEAD_TIME, MIN_LOOKAHEAD
+from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.shader_polygon import draw_polygon
+from openpilot.system.ui.lib.text_measure import measure_text_cached
+
+BLENDFUL_COLOR = (0, 200, 255)
+FULL_BAR = 1.0
+
+
+class BlendfulRenderer:
+  def __init__(self, readout: bool = True):
+    self._readout = readout
+    self._center_points = np.empty((0, 2), dtype=np.float32)
+    self._trust = 0.0
+    self._font = gui_app.font(FontWeight.BOLD)
+    self._pull_display = FirstOrderFilter(0.0, 0.5, DT_MDL)
+    self._trust_display = FirstOrderFilter(0.0, 0.5, DT_MDL)
+    self._alpha = FirstOrderFilter(0.0, 0.3, DT_MDL)
+
+  def update(self, renderer, max_idx: int, *map_args) -> None:
+    blendful = ui_state.sm['modelDataV2SP']
+    if ui_state.sm.updated['modelDataV2SP']:
+      self._alpha.update(float(blendful.blendfulTrust > 0.0))
+      if blendful.blendfulTrust > 0.0:
+        self._trust = blendful.blendfulTrust
+      if self._readout:
+        lookahead = max(ui_state.sm['carState'].vEgo * LOOKAHEAD_TIME, MIN_LOOKAHEAD)
+        self._pull_display.update(blendful.blendfulCurvature * lookahead ** 2 / 2)
+        self._trust_display.update(self._trust)
+
+    left, right = renderer._lane_lines[1].raw_points, renderer._lane_lines[2].raw_points
+    stds = list(ui_state.sm['modelV2'].laneLineStds)
+    if self._alpha.x < 0.01 or len(left) == 0 or len(right) != len(left) or len(stds) < 3:
+      self._center_points = np.empty((0, 2), dtype=np.float32)
+      return
+
+    l_prob, r_prob = blendful.blendfulLeftProb, blendful.blendfulRightProb
+    if blendful.blendfulTrust > 0.0 and l_prob + r_prob > 0.0:
+      half_width = np.clip(right[:, 1] - left[:, 1], *LANE_WIDTH_CLIP) / 2 if l_prob * r_prob > 0.5 else blendful.blendfulLaneWidth / 2
+      l_var = stds[1] ** 2 / max(l_prob, 1e-4)
+      r_var = stds[2] ** 2 / max(r_prob, 1e-4)
+      center = left.copy()
+      center[:, 1] = (r_var * (left[:, 1] + half_width) + l_var * (right[:, 1] - half_width)) / (l_var + r_var)
+      self._center_points = renderer._map_line_to_polygon(center, 0.15, 0.0, max_idx, *map_args)
+
+  def _color(self, rgb, alpha: float) -> rl.Color:
+    return rl.Color(*rgb, int(alpha * self._alpha.x))
+
+  def draw(self, rect: rl.Rectangle) -> None:
+    if self._center_points.size and self._trust > 0.05:
+      draw_polygon(rect, self._center_points, self._color(BLENDFUL_COLOR, np.clip(self._trust, 0.2, 1.0) * 255))
+    if self._center_points.size and self._readout:
+      self._draw_readout(rect)
+
+  def _draw_readout(self, rect: rl.Rectangle) -> None:
+    pull, trust = self._pull_display.x, self._trust_display.x
+    w, h = 190, 76
+    x, y = rect.x + (rect.width - w) / 2, rect.y + 10
+    cx, cy = x + w / 2, y + 30
+    rl.draw_rectangle_rounded(rl.Rectangle(x, y, w, h), 0.5, 12, self._color((0, 0, 0), 170))
+
+    text = f"{abs(pull) * 100:.0f}"
+    text_w = measure_text_cached(self._font, text, 44, 0).x
+    rl.draw_text_ex(self._font, text, rl.Vector2(cx - text_w / 2, cy - 24), 44, 0, self._color((255, 255, 255), 255))
+
+    active = self._color(BLENDFUL_COLOR, np.clip(abs(pull) / 0.1, 0.0, 1.0) * 255)
+    idle = self._color((255, 255, 255), 40)
+    lx, rx = x + 18, x + w - 18
+    rl.draw_triangle(rl.Vector2(lx, cy), rl.Vector2(lx + 16, cy + 12), rl.Vector2(lx + 16, cy - 12), active if pull < 0 else idle)
+    rl.draw_triangle(rl.Vector2(rx - 16, cy - 12), rl.Vector2(rx - 16, cy + 12), rl.Vector2(rx, cy), active if pull > 0 else idle)
+
+    bar = rl.Rectangle(x + 24, y + h - 16, w - 48, 6)
+    rl.draw_rectangle_rounded(bar, 1.0, 6, self._color((255, 255, 255), 40))
+    rl.draw_rectangle(int(cx - 1), int(bar.y - 2), 2, int(bar.height + 4), self._color((255, 255, 255), 120))
+    fill = bar.width / 2 * float(np.sqrt(np.clip(abs(pull) / FULL_BAR, 0.0, 1.0)))
+    if fill > 1:
+      fill_x = cx if pull > 0 else cx - fill
+      rl.draw_rectangle_rounded(rl.Rectangle(fill_x, bar.y, fill, bar.height), 1.0, 6,
+                                self._color(BLENDFUL_COLOR, np.clip(0.3 + 0.7 * trust, 0.0, 1.0) * 255))

openpilot/selfdrive/ui/sunnypilot/onroad/model_renderer.py

@@ -6,6 +6,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
+from openpilot.selfdrive.ui.sunnypilot.onroad.blendful import BlendfulRenderer
 from openpilot.selfdrive.ui.sunnypilot.onroad.chevron_metrics import ChevronMetrics
 from openpilot.selfdrive.ui.sunnypilot.onroad.rainbow_path import RainbowPath
 from openpilot.selfdrive.ui.sunnypilot.ui_state import MADSState
@@ -16,6 +17,7 @@ class ModelRendererSP:
   def __init__(self):
     self.rainbow_path = RainbowPath()
     self.chevron_metrics = ChevronMetrics()
+    self.blendful = BlendfulRenderer(readout=False)
     self._width_filter = FirstOrderFilter(0.9, 0.1, 1 / gui_app.target_fps)
 
   @property

openpilot/selfdrive/ui/sunnypilot/ui_state.py

@@ -36,7 +36,7 @@ class UIStateSP:
     self.is_sp_release: bool = self.params.get_bool("IsReleaseSpBranch")
     self.sm_services_ext = [
       "modelManagerSP", "selfdriveStateSP", "longitudinalPlanSP", "backupManagerSP",
-      "lateralTorqueParameters", "carStateSP", "liveMapDataSP", "carParamsSP", "lateralDelay"
+      "lateralTorqueParameters", "carStateSP", "liveMapDataSP", "carParamsSP", "lateralDelay", "modelDataV2SP"
     ]
 
     self.sunnylink_state = SunnylinkState()

openpilot/sunnypilot/modeld_v2/blendful.py

@@ -0,0 +1,140 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import numpy as np
+
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.common.realtime import DT_MDL
+from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
+
+LANE_WIDTH_DEFAULT = 3.6  # m
+LANE_WIDTH_CLIP = (2.8, 4.5)  # m
+LOOKAHEAD_TIME = 1.5  # s
+MIN_LOOKAHEAD = 10.0  # m
+MIN_SPEED = 5.0  # m/s
+LINE_STD = (0.2, 0.6)
+POINT_STD = 0.1
+YIELD = (0.2, 0.45)
+X_IDXS = np.array(ModelConstants.X_IDXS)
+
+
+class Blendful:
+  def __init__(self):
+    self.lane_width = FirstOrderFilter(LANE_WIDTH_DEFAULT, 10.0, DT_MDL, initialized=False)
+    self.line_gone = FirstOrderFilter(np.zeros(2), 2.0, DT_MDL)
+    self.line_probs_slow = FirstOrderFilter(np.zeros(2), 3.0, DT_MDL)
+    self.line_probs = FirstOrderFilter(np.zeros(2), 0.5, DT_MDL)
+    self.usual_offset = FirstOrderFilter(0.0, 10.0, DT_MDL, initialized=False)
+    self.going_around = False
+    self.since_turning = 0.0
+    self.trust = 0.0
+    self.line_trust = 0.0
+    self.dk = 0.0 # delta kappa; difference between lane center and model plan
+    self.l_prob = 0.0
+    self.r_prob = 0.0
+    self.model_curvature = 0.0
+    self.smooth = 0.0 # smoothens lanelines below .53 lat accel
+    self.curvature = 0.0
+
+  def update(self, model_output: dict[str, np.ndarray], v_ego: float, active: bool, model_curvature: float) -> float:
+    self.l_prob = self.r_prob = 0.0
+    fading = not active or v_ego < MIN_SPEED
+    if fading:
+      self.trust = self.line_trust = 0.0
+      self.usual_offset.initialized = False
+      self.lane_width.initialized = False
+      self.going_around = False
+    self.model_curvature = model_curvature
+    target = 0.0 if fading else self._target(model_output, v_ego, model_curvature)
+    target = self.smooth = self.smooth + (target - self.smooth) * DT_MDL / (DT_MDL + np.interp(abs(model_curvature) * v_ego ** 2, [0.18, 0.53], [0.5, 0.0]))
+    build = 0.5 / max(v_ego, 1.0) ** 2 * DT_MDL  # jerk over 1 second
+    release = (0.5 if fading else 2.0) / max(v_ego, 1.0) ** 2 * DT_MDL  # lets go fast, but past zero it builds like any other pull
+    k = self.curvature
+    self.curvature = float(np.clip(target, max(k - release, -build) if k > 0 else k - build, min(k + release, build) if k < 0 else k + build))
+    return self.curvature
+
+  def _target(self, model_output: dict[str, np.ndarray], v_ego: float, model_curvature: float) -> float:
+    lookahead = max(v_ego * LOOKAHEAD_TIME, MIN_LOOKAHEAD)
+    lines = model_output['lane_lines'][0, :, :, 0]
+    stds = model_output['lane_lines_stds'][0, :, :, 0]
+
+    lone_weight = np.interp(abs(model_curvature) * v_ego ** 2, [0.18, 0.53], [0.5, 1.0])  # 0.5 on straights, 1.0 in curves
+    probs = self.line_probs.update(model_output['lane_lines_prob'][0, 1::2][1:3])
+    absent = (probs < 0.3) & (lone_weight > 0.5)
+    gone = self.line_gone.update((model_output['lane_lines_prob'][0, 1::2][1:3] < 0.3).astype(float))
+    missing = np.where(absent, 1.0, gone)
+    dropping = bool(np.any((probs < 0.6 * self.line_probs_slow.update(model_output['lane_lines_prob'][0, 1::2][1:3])) & ~absent))
+    l_prob = float(probs[0] * np.interp(np.interp(0.5 * lookahead, X_IDXS, stds[1]), LINE_STD, [1.0, 0.0]))
+    r_prob = float(probs[1] * np.interp(np.interp(0.5 * lookahead, X_IDXS, stds[2]), LINE_STD, [1.0, 0.0]))
+    self.l_prob, self.r_prob = l_prob, r_prob
+    # one line steadily on its own still centers at lone_weight. a line that is disappearing gives control back to laneless
+    if missing[1] > 0.8 and l_prob > 0.5:
+      lane_trust = l_prob * lone_weight
+    elif missing[0] > 0.8 and r_prob > 0.5:
+      lane_trust = r_prob * lone_weight
+    elif dropping:
+      lane_trust = min(l_prob, r_prob)
+    else:
+      lane_trust = l_prob + r_prob - l_prob * r_prob
+    if lone_weight > 0.5 and lane_trust < self.line_trust:
+      lane_trust = self.line_trust + (lane_trust - self.line_trust) * DT_MDL / (lone_weight - 0.5 + DT_MDL)
+    self.line_trust = lane_trust
+    if lane_trust <= 0.0:
+      self.trust = 0.0
+      return 0.0
+    if l_prob + r_prob <= 0.0:  # keep the last fit while trust lets go
+      self.trust = min(self.trust, lane_trust)
+      return float(self.trust * self.dk)
+
+    win = (X_IDXS >= 0.5 * lookahead) & (X_IDXS <= 1.5 * lookahead)
+    x = X_IDXS[win]
+    half_width = self.lane_width.x / 2
+    if l_prob * r_prob > 0.5:
+      width = np.interp(lookahead, X_IDXS, lines[2]) - np.interp(lookahead, X_IDXS, lines[1])
+      self.lane_width.update(float(np.clip(width, *LANE_WIDTH_CLIP)))
+      half_width = np.clip(lines[2, win] - lines[1, win], *LANE_WIDTH_CLIP) / 2
+    l_var = stds[1, win] ** 2 / max(l_prob, 1e-4)
+    r_var = stds[2, win] ** 2 / max(r_prob, 1e-4)
+    center = (r_var * (lines[1, win] + half_width) + l_var * (lines[2, win] - half_width)) / (l_var + r_var)
+    weight = 1.0 / (l_var * r_var / (l_var + r_var) + POINT_STD ** 2)
+
+    # Fit curvature to place the plant in center: e ~ dk x^2 / 2
+    plan_x = np.interp(x, model_output['plan'][0, :, 0], model_output['plan'][0, :, 1])
+    plan_share = np.interp(abs(model_curvature) * lookahead ** 2 / 2, [0.5, 2.0], [0.3, 0.5]) # blending up to 50% plan with action during sharp curvature
+    error = np.clip(center - ((1 - plan_share) * model_curvature * x ** 2 / 2 + plan_share * plan_x), -1.0, 1.0)
+    dk = 2.0 * np.sum(weight * error * x ** 2) / np.sum(weight * x ** 4)
+
+    # A model planning to end up further from its usual spot in the lane than the plant is now is likely going around something
+    hw_now = np.clip(lines[2, 0] - lines[1, 0], *LANE_WIDTH_CLIP) / 2 if l_prob * r_prob > 0.5 else self.lane_width.x / 2
+    center_now = (l_prob * (lines[1, 0] + hw_now) + r_prob * (lines[2, 0] - hw_now)) / (l_prob + r_prob)
+    plan_y = np.interp(lookahead, model_output['plan'][0, :, 0], model_output['plan'][0, :, 1])
+    offset = plan_y - np.interp(lookahead, x, center)
+    if min(gone) > 0.5:  # both lines gone: turns, intersections, relearn ideal model position
+      self.usual_offset.initialized = False
+    usual = self.usual_offset.x if self.usual_offset.initialized else offset
+    swerve = abs(offset - usual) - 0.25 * abs(model_curvature) * lookahead ** 2 / 2
+    away = swerve - abs(-center_now - usual)
+    lane_curvature = 2.0 * np.polyfit(x, center, 2)[0]
+    inside_curve = abs(lane_curvature) * lookahead ** 2 / 2 > 0.1 and (offset - usual) * lane_curvature > 0
+    self.since_turning = 0.0 if abs(model_curvature) * lookahead ** 2 / 2 > 0.5 else self.since_turning + DT_MDL
+    if inside_curve:
+      self.going_around = False
+    elif away > YIELD[0] and self.since_turning > 2.0:
+      self.going_around = True
+    elif swerve < YIELD[0] / 2:
+      self.going_around = False
+    self.usual_offset.update_alpha(20.0 if self.going_around else 10.0)
+    self.usual_offset.update(offset)
+    if inside_curve:
+      lane_trust *= np.interp(abs(offset) - abs(center_now) - 0.25 * abs(model_curvature) * lookahead ** 2 / 2, (0.5, 1.0), [1.0, 0.0])
+    else:
+      lane_trust *= np.interp(swerve, (YIELD[0] / 2, YIELD[0]), [1.0, 0.0]) if self.going_around else np.interp(away, YIELD, [1.0, 0.0])
+
+    if lane_trust < self.trust:
+      dk = float(np.clip(dk, min(self.dk, 0.0), max(self.dk, 0.0)))
+    self.trust = lane_trust if lane_trust < self.trust else self.trust + (lane_trust - self.trust) * DT_MDL / (1.5 + DT_MDL)
+    self.dk = dk
+    return float(self.trust * dk)

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -7,6 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 from collections.abc import Callable
+from functools import partial
 import gc
 import os
 os.environ['GMMU'] = '0'
@@ -39,6 +40,7 @@ from openpilot.selfdrive.modeld.modeld import ChestnutGpuState
 
 from openpilot.sunnypilot.modeld_v2.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState, get_curvature_from_output
 from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser
+from openpilot.sunnypilot.modeld_v2.blendful import Blendful
 from openpilot.sunnypilot.modeld_v2.constants import ModelConstants, Plan
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
@@ -228,7 +230,8 @@ class ModelState(ModelStateBase):
     return outputs
 
   def get_action_from_model(self, model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
-                            lat_action_t: float, long_action_t: float, v_ego: float) -> log.ModelDataV2.Action:
+                            lat_action_t: float, long_action_t: float, v_ego: float,
+                            blendful: Callable[[float], float] | None = None) -> log.ModelDataV2.Action:
     if 'action' not in model_output:
       plan = model_output['plan'][0]
       desired_accel = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
@@ -240,6 +243,8 @@ class ModelState(ModelStateBase):
     else:
       desired_accel = model_output['action'][0, 1]
       desired_curvature = model_output['action'][0, 0] / (max(1.0, v_ego))**2
+    if blendful is not None:
+      desired_curvature += blendful(desired_curvature)
 
     stop = v_ego < 0.3 and desired_accel < 0.1
     desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, self.LONG_SMOOTH_SECONDS)
@@ -366,6 +371,8 @@ def main(demo=False):
   DH = DesireHelper()
   meta_constants = load_meta_constants()
   RELC = RoadEdgeLaneChangeController()
+  blendful = Blendful()
+  blendful_enabled = False
 
   while True:
     # Keep receiving frames until we are at least 1 frame ahead of previous extra frame
@@ -409,6 +416,7 @@ def main(demo=False):
       model.lat_delay = get_lat_delay(params, sm["lateralDelay"].lateralDelay)
       model.PLANPLUS_CONTROL = params.get("PlanplusControl", return_default=True)
       camera_offset_helper.set_offset(params.get("CameraOffset", return_default=True))
+      blendful_enabled = params.get_bool("Blendful")
     lat_delay = model.lat_delay + model.LAT_SMOOTH_SECONDS
     if sm.updated["extrinsicsCalibration"] and sm.seen['narrowRoadCameraState'] and sm.seen['deviceState']:
       device_from_calib_euler = np.array(sm["extrinsicsCalibration"].rpyCalib, dtype=np.float32)
@@ -482,7 +490,9 @@ def main(demo=False):
 
       if not sm['carControl'].latActive:
         prev_action.desiredCurvature = sm['controlsState'].curvature
-      action = model.get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego)
+      blendful_active = blendful_enabled and desire == log.Desire.none and DH.lane_change_state == log.LaneChangeState.off
+      action = model.get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego,
+                                           partial(blendful.update, model_output, v_ego, blendful_active))
       prev_action = action
       fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
                      publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
@@ -499,6 +509,13 @@ def main(demo=False):
       modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
       mdv2sp_send.valid = modelv2_send.valid
       mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction
+      mdv2sp_send.modelDataV2SP.blendfulCurvature = blendful.curvature
+      mdv2sp_send.modelDataV2SP.blendfulTrust = float(blendful.trust)
+      mdv2sp_send.modelDataV2SP.modelCurvature = float(blendful.model_curvature)
+      mdv2sp_send.modelDataV2SP.blendfulGoingAround = bool(blendful.going_around)
+      mdv2sp_send.modelDataV2SP.blendfulLeftProb = blendful.l_prob
+      mdv2sp_send.modelDataV2SP.blendfulRightProb = blendful.r_prob
+      mdv2sp_send.modelDataV2SP.blendfulLaneWidth = float(blendful.lane_width.x)
       drivingdata_send.drivingModelData.meta.laneChangeState = DH.lane_change_state
       drivingdata_send.drivingModelData.meta.laneChangeDirection = DH.lane_change_direction
 

openpilot/sunnypilot/sunnylink/settings_ui.json

@@ -2034,8 +2034,14 @@
         {
           "id": "model_behavior",
           "title": "Model Behavior",
-          "description": "Lane desire and lead-vehicle awareness tuning",
+          "description": "Lane desire, Blendful lane centering, and lead-vehicle awareness tuning",
           "items": [
+            {
+              "key": "Blendful",
+              "widget": "toggle",
+              "title": "Blendful Mode",
+              "description": "Nudges the car toward the lane center when the lane lines are clear. Drives laneless when they are not."
+            },
             {
               "key": "LaneTurnDesire",
               "widget": "toggle",

openpilot/sunnypilot/sunnylink/settings_ui_src/pages/models.yaml

@@ -9,8 +9,12 @@ description: Driving model behavior and camera calibration
 sections:
 - id: model_behavior
   title: Model Behavior
-  description: Lane desire and lead-vehicle awareness tuning
+  description: Lane desire, Blendful lane centering, and lead-vehicle awareness tuning
   items:
+  - key: Blendful
+    widget: toggle
+    title: Blendful Mode
+    description: Nudges the car toward the lane center when the lane lines are clear. Drives laneless when they are not.
   - key: LaneTurnDesire
     widget: toggle
     title: Use Lane Turn Desires