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24 Commits

Author SHA1 Message Date
firestar5683 fbfa15e6b9 Update tinygrad_modeld.py 2025-05-31 13:00:31 -05:00
firestar5683 da493a9e50 Revert "Update launch_chffrplus.sh"
This reverts commit b4d070fbd58fe245be2ae7e1814589e0cfec1ad3.
2025-05-30 11:25:14 -05:00
firestar5683 6921b3bd11 Delete not_vetted 2025-05-30 11:21:47 -05:00
FrogAi d4620a8c7b agnos 10.1.1 2025-05-26 20:47:03 -05:00
firestar5683 1c38f80ac8 Vikander Model 2025-05-24 15:03:59 -05:00
firestar5683 277a287c2c Enable Uploads 2025-05-18 13:22:50 -05:00
firestar5683 36df8c812c Restore Sanity
AHHHHHHH
2025-05-18 13:22:50 -05:00
firestar5683 529c8b8dbf Update interface.py 2025-05-12 15:11:21 -05:00
firestar5683 77616a4205 Update interface.py 2025-05-12 14:21:49 -05:00
firestar5683 02eab9bfed Revert "Update interface.py"
This reverts commit c54742b762.
2025-05-12 13:23:54 -05:00
firestar5683 c54742b762 Update interface.py
Update interface.py

Update interface.py
2025-05-12 13:23:49 -05:00
firestar5683 9898e5ac4c Update process_config.py 2025-05-06 17:17:51 -05:00
firestar5683 6e77a80772 2017 2025-05-06 16:43:37 -05:00
firestar5683 68b97a87db Revert "Reapply "Tomb Raider 6""
This reverts commit f3de8169b9.
2025-05-06 16:32:19 -05:00
firestar5683 9d6211ee7c StarPilot 2025-05-06 16:31:36 -05:00
firestar5683 f3de8169b9 Reapply "Tomb Raider 6" 2025-05-06 16:17:43 -05:00
firestar5683 5095ed9d23 Update carcontroller.py 2025-05-06 16:16:55 -05:00
firestar5683 a11d2958df Logging 2025-04-23 23:15:03 -05:00
firestar5683 30ff869627 TorqueTune/Paddle 2025-04-23 16:19:57 -05:00
FrogAi d01cd48bee Compile FrogPilot 2025-04-20 15:12:33 -07:00
FrogAi 4eefdc3268 Update 2025-04-20 22:00:46 +00:00
FrogAi 94010a84ab FrogPilot 0.9.7 2025-04-15 22:03:14 -07:00
FrogAi 24eac8f4be OPGM
Co-Authored-By: Eric Brown <13560103+nworb-cire@users.noreply.github.com>
2025-04-12 12:00:00 -07:00
FrogAi 3ec3c9702a openpilot v0.9.7 release
date: 2024-06-11T01:36:39
master commit: f8cb04e4a8b032b72a909f68b808a50936184bee
2025-04-12 12:00:00 -07:00
3441 changed files with 951002 additions and 312815 deletions
+264
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@@ -0,0 +1,264 @@
name: Compile FrogPilot
on:
workflow_dispatch:
inputs:
not_vetted:
description: "This branch is not vetted"
required: false
default: "false"
type: boolean
publish_frogpilot:
description: "Push to FrogPilot"
required: false
default: "false"
type: boolean
publish_staging:
description: "Push to FrogPilot-Staging"
required: false
default: "false"
type: boolean
publish_testing:
description: "Push to FrogPilot-Testing"
required: false
default: "false"
type: boolean
publish_custom_branch:
description: "Push to custom branch:"
required: false
default: ""
type: string
update_translations:
description: "Update missing translations"
required: false
default: "false"
type: boolean
vet_existing_translations:
description: "Vet existing translations"
required: false
default: "false"
type: boolean
env:
BASEDIR: "${{ github.workspace }}"
BUILD_DIR: /data/openpilot
OPENAI_API_KEY: "${{ secrets.OPENAI_API_KEY }}"
jobs:
get_branch:
runs-on: [self-hosted, c3x]
outputs:
branch: ${{ steps.get_branch.outputs.branch }}
python_version: ${{ steps.get_python_version.outputs.python_version }}
steps:
- name: Determine Current Branch on Runner
id: get_branch
run: |
cd $BUILD_DIR
BRANCH=$(git rev-parse --abbrev-ref HEAD)
echo "branch=$BRANCH" >> $GITHUB_OUTPUT
- name: Get Python Version from Runner
id: get_python_version
run: |
PYTHON_VERSION=$(tr -d '[:space:]' < "$BUILD_DIR/.python-version")
echo "python_version=$PYTHON_VERSION" >> $GITHUB_OUTPUT
translate:
if: inputs.update_translations
needs: get_branch
runs-on: ubuntu-latest
steps:
- name: Configure Git Identity
run: |
git config --global user.name "${{ github.actor }}"
git config --global user.email "${{ github.actor }}@users.noreply.github.com"
- name: Checkout Required Files
uses: actions/checkout@v4
with:
ref: ${{ needs.get_branch.outputs.branch }}
sparse-checkout: |
frogpilot/ui/
selfdrive/controls/lib/alerts_offroad.json
selfdrive/ui/
selfdrive/ui/update_translations.py
selfdrive/ui/translations/
selfdrive/ui/translations/auto_translate.py
- name: Set Up Python
uses: actions/setup-python@v4
with:
python-version: "${{ needs.get_branch.outputs.python_version }}"
- name: Install Dependencies
run: pip install requests
- name: Install Qt5 Tools
run: sudo apt update && sudo apt install -y qttools5-dev-tools
- name: Update Translations
run: python selfdrive/ui/update_translations.py --vanish
- name: Translate Missing or Outdated Translations
run: python selfdrive/ui/translations/auto_translate.py --all-files $([[ "${{ github.event.inputs.vet_existing_translations }}" == "true" ]] && echo "--vet-translations")
- name: Commit and Push Translation Updates
run: |
if git diff --quiet selfdrive/ui/translations/*.ts; then
echo "No translation updates detected."
exit 0
fi
git fetch --unshallow origin ${{ needs.get_branch.outputs.branch }}
git checkout ${{ needs.get_branch.outputs.branch }}
git add selfdrive/ui/translations/*.ts
git commit --amend --no-edit
git push --force origin ${{ needs.get_branch.outputs.branch }}
build_and_push:
needs:
- get_branch
- translate
if: always()
runs-on: [self-hosted, c3x]
permissions:
contents: write
defaults:
run:
working-directory: ${{ env.BUILD_DIR }}
steps:
- name: Configure Git Identity
run: |
git config --global http.postBuffer 104857600
git config --global user.name "${{ github.actor }}"
git config --global user.email "${{ github.actor }}@users.noreply.github.com"
- name: Update Repository
run: |
git remote set-url origin https://${{ secrets.PERSONAL_ACCESS_TOKEN }}@github.com/FrogAi/FrogPilot.git
if [ "${{ github.event.inputs.update_translations }}" = "true" ]; then
git fetch origin ${{ needs.get_branch.outputs.branch }}
git reset --hard origin/${{ needs.get_branch.outputs.branch }}
git pull origin ${{ needs.get_branch.outputs.branch }}
fi
- name: Take Ownership of Build
run: |
sudo chown -R $(whoami):$(whoami) .
- name: Finalize Build
run: |
rm -f .clang-tidy
rm -f .dockerignore
rm -f .editorconfig
rm -f .gitattributes
rm -f .gitmodules
rm -f .lfsconfig
rm -f .overlay_init
rm -f .pre-commit-config.yaml
rm -f .sconsign.dblite
rm -f codecov.yml
rm -f conftest.py
rm -f poetry.lock
rm -f pyproject.toml
rm -f teleoprtc
rm -f Dockerfile.openpilot
rm -f Dockerfile.openpilot_base
rm -f Jenkinsfile
rm -f panda/board/obj/.placeholder
rm -f panda/board/obj/bootstub.panda.elf
rm -f panda/board/obj/bootstub.panda_h7.elf
rm -f panda/board/obj/panda.bin
rm -f panda/board/obj/panda.elf
rm -f panda/board/obj/panda_h7.bin
rm -f panda/board/obj/panda_h7.elf
rm -f panda/board/obj/version
find . -name '*.a' -delete
find . -name '*.cc' -delete
find . -name '*.o' -delete
find . -name '*.onnx' -delete
find . -name '*.os' -delete
find . -name '*.pyc' -delete
find . -name 'moc_*' -delete
find . -name '*.h' | while read -r header; do
if [[ "$header" != *"common/version.h" && "$header" != *"system/camerad/sensors/"* ]]; then
rm -f "$header"
fi
done
rm -rf .devcontainer/
rm -rf .vscode/
rm -rf body/
rm -rf opendbc/generator/
rm -rf release/
rm -rf scripts/
rm -rf site_scons/
rm -rf teleoprtc_repo/
find .github -mindepth 1 -maxdepth 1 ! -name 'workflows' -exec rm -rf {} +
find .github/workflows -mindepth 1 ! \( \
-type f \( \
-name 'compile_frogpilot.yaml' -o \
-name 'review_pull_request.yaml' -o \
-name 'schedule_update.yaml' -o \
-name 'update_pr_branch.yaml' -o \
-name 'update_release_branch.yaml' \
\) \
\) -exec rm -rf {} +
find panda/board/jungle -type f ! -name '__init__.py' -delete
find panda/board/jungle -type d -empty -delete
find third_party/ -name '*x86*' -exec rm -rf {} +
find third_party/ -name '*Darwin*' -exec rm -rf {} +
find tools/ -mindepth 1 -maxdepth 1 ! \( -name '__init__.py' -o -name 'bodyteleop' -o -name 'lib' -o -name 'scripts' \) -exec rm -rf {} +
find . -name 'SConstruct' -delete
find . -name 'SConscript' -delete
find . -type d \( -iname "debug" -o -iname "test" -o -iname "tests" \) -exec rm -rf {} +
find . -type d -empty ! -path "./.git*" -delete
find . -type d -name '__pycache__' -exec rm -rf {} +
find . -type f -regex '.*matlab.*\.md' -delete
touch prebuilt
if [ "${{ github.event.inputs.not_vetted }}" = "true" ]; then
touch not_vetted
fi
- name: Add the update_date file
if: github.event.inputs.publish_staging == 'true'
run: |
curl -fLsS https://raw.githubusercontent.com/FrogAi/FrogPilot/FrogPilot-Staging/.github/update_date -o .github/update_date || echo "No update_date found, skipping."
- name: Commit Build
run: |
git add -f .
git commit -m "Compile FrogPilot"
git push --force origin HEAD
if [ "${{ github.event.inputs.publish_frogpilot }}" = "true" ]; then
git push --force origin HEAD:"FrogPilot"
fi
if [ "${{ github.event.inputs.publish_staging }}" = "true" ]; then
git push --force origin HEAD:"FrogPilot-Staging"
fi
if [ "${{ github.event.inputs.publish_testing }}" = "true" ]; then
git push --force origin HEAD:"FrogPilot-Testing"
fi
if [ -n "${{ github.event.inputs.publish_custom_branch }}" ]; then
git push --force origin HEAD:"${{ github.event.inputs.publish_custom_branch }}"
fi
@@ -0,0 +1,34 @@
name: Review Pull Request
on:
pull_request_target:
types: [opened, reopened]
env:
GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
jobs:
pr_check:
runs-on: ubuntu-latest
steps:
- name: Exit If PR Opened by FrogAi
if: ${{ github.actor == 'FrogAi' }}
run: |
echo PR opened or reopened by FrogAi. No action needed.
exit 0
- name: Close PR for Invalid Target Branch
if: ${{ github.base_ref != 'MAKE-PRS-HERE' }}
run: |
gh api repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/comments \
-f body="Please submit your pull request to the \"MAKE-PRS-HERE\" branch."
gh api repos/${{ github.repository }}/pulls/${{ github.event.pull_request.number }} \
-X PATCH \
-f state="closed"
- name: Acknowledge PR for Valid Target Branch
if: ${{ github.base_ref == 'MAKE-PRS-HERE' }}
run: |
gh api repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/comments \
-f body="Thank you for your PR! If you're not already in the FrogPilot Discord, [feel free to join](https://discord.FrogPilot.download) and let me know you've opened a PR!"
+57
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@@ -0,0 +1,57 @@
name: Schedule FrogPilot Update
on:
workflow_dispatch:
inputs:
scheduled_date:
description: "Enter the date to update the \"FrogPilot\" branch (YYYY-MM-DD)"
required: true
env:
BRANCH: FrogPilot-Staging
jobs:
schedule-update:
runs-on: ubuntu-latest
steps:
- name: Configure Git Identity
run: |
git config --global user.name "${{ github.actor }}"
git config --global user.email "${{ github.actor }}@users.noreply.github.com"
- name: Checkout ${{ env.BRANCH }}
uses: actions/checkout@v3
with:
ref: ${{ env.BRANCH }}
fetch-depth: 3
- name: Schedule Update for ${{ github.event.inputs.scheduled_date }}
run: |
echo "${{ github.event.inputs.scheduled_date }}" > .github/update_date
git add .github/update_date
- name: Get Target Commit (Second Most Recent)
id: get_target
run: |
TARGET_COMMIT=$(git rev-parse HEAD~1)
AUTHOR_DATE=$(git show -s --format=%aD "$TARGET_COMMIT")
COMMITTER_DATE=$(git show -s --format=%cD "$TARGET_COMMIT")
echo "AUTHOR_DATE=$AUTHOR_DATE" >> $GITHUB_ENV
echo "COMMITTER_DATE=$COMMITTER_DATE" >> $GITHUB_ENV
echo "TARGET_COMMIT=$TARGET_COMMIT" >> $GITHUB_ENV
- name: Create Fixup Commit for ${{ env.TARGET_COMMIT }}
run: git commit --fixup="${{ env.TARGET_COMMIT }}"
- name: Autosquash Fixup into Target Commit
run: |
GIT_SEQUENCE_EDITOR=: git rebase --autosquash -i HEAD~3
- name: Restore Timestamps on Final Two Commits
run: |
git rebase --exec "GIT_COMMITTER_DATE='${{ env.COMMITTER_DATE }}' git commit --amend --no-edit --date='${{ env.AUTHOR_DATE }}'" HEAD~2
- name: Push Updated ${{ env.BRANCH }} Branch
run: git push origin "${{ env.BRANCH }}" --force-with-lease
+99
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@@ -0,0 +1,99 @@
name: Update MAKE-PRS-HERE
on:
push:
branches:
- FrogPilot-Staging
env:
SOURCE_BRANCH: FrogPilot-Staging
TARGET_BRANCH: MAKE-PRS-HERE
jobs:
update_branch:
runs-on: ubuntu-latest
steps:
- name: Configure Git Identity
run: |
git config --global user.name "${{ github.actor }}"
git config --global user.email "${{ github.actor }}@users.noreply.github.com"
- name: Checkout ${{ env.SOURCE_BRANCH }}
uses: actions/checkout@v3
with:
ref: "${{ env.SOURCE_BRANCH }}"
fetch-depth: 0
- name: Find "Compile FrogPilot" Commit in ${{ env.SOURCE_BRANCH }}
id: find_parent
run: |
COMMIT=$(git rev-list HEAD -n 1 --grep="Compile FrogPilot")
if [ -z "$COMMIT" ]; then
echo "Commit with message 'Compile FrogPilot' not found." >&2
exit 1
fi
PARENT=$(git rev-list --parents -n 1 "$COMMIT" | awk '{print $2}')
if [ -z "$PARENT" ]; then
echo "Parent commit not found." >&2
exit 1
fi
echo "Compile commit: $COMMIT"
echo "Parent commit: $PARENT"
echo "parent_commit=$PARENT" >> "$GITHUB_OUTPUT"
echo "compile_commit=$COMMIT" >> "$GITHUB_OUTPUT"
- name: Checkout ${{ env.TARGET_BRANCH }}
run: |
git fetch origin "${{ env.TARGET_BRANCH }}"
git checkout "${{ env.TARGET_BRANCH }}"
- name: Clean ${{ env.TARGET_BRANCH }} and Apply Updates from ${{ env.SOURCE_BRANCH }}
run: |
git rm -r --ignore-unmatch .
git clean -fdx
PARENT_COMMIT="${{ steps.find_parent.outputs.parent_commit }}"
git checkout "$PARENT_COMMIT" -- .
COMPILE_COMMIT="${{ steps.find_parent.outputs.compile_commit }}"
SOURCE_HEAD=$(git rev-parse "origin/${{ env.SOURCE_BRANCH }}")
if [ "$COMPILE_COMMIT" != "$SOURCE_HEAD" ]; then
echo "Applying changes from '${{ env.SOURCE_BRANCH }}' branch..."
git cherry-pick --no-commit "${COMPILE_COMMIT}".."$SOURCE_HEAD"
fi
rm -f .github/update_date
git add --all
- name: Commit and Push to ${{ env.TARGET_BRANCH }}
run: |
TZ_VALUE="America/Phoenix"
current_day=$(TZ="$TZ_VALUE" date +"%-d")
month=$(TZ="$TZ_VALUE" date +"%B")
year=$(TZ="$TZ_VALUE" date +"%Y")
if [[ "$current_day" =~ ^1[123]$ ]]; then
suffix="th"
else
case $(($current_day % 10)) in
1) suffix="st" ;;
2) suffix="nd" ;;
3) suffix="rd" ;;
*) suffix="th" ;;
esac
fi
commit_message="${month} ${current_day}${suffix}, ${year} Update"
echo "Commit message: $commit_message"
if git log --oneline --format="%s" | grep -q "$commit_message"; then
echo "Existing commit found with message: $commit_message"
git commit --amend --no-edit
else
git commit -m "$commit_message"
fi
git push origin "${{ env.TARGET_BRANCH }}" --force
@@ -0,0 +1,132 @@
name: Update FrogPilot Branch
on:
schedule:
- cron: "0 18 * * *"
env:
BRANCH_FROGPILOT: FrogPilot
BRANCH_PREVIOUS: FrogPilot-Previous
BRANCH_STAGING: FrogPilot-Staging
TZ: America/Phoenix
UPDATE_FILE: .github/update_date
jobs:
check_update:
runs-on: ubuntu-latest
outputs:
update_due: ${{ steps.check_update.outputs.update_due }}
scheduled_date: ${{ steps.check_update.outputs.scheduled_date }}
steps:
- name: Download the 'update_date' File
id: download_update
run: |
curl -fLsS "https://raw.githubusercontent.com/FrogAi/FrogPilot/${{ env.BRANCH_STAGING }}/${{ env.UPDATE_FILE }}" -o update_date || touch update_date_missing
- name: Check If Update Is Due
id: check_update
run: |
if [ -f update_date_missing ]; then
echo "No update_date file found in FrogPilot-Staging."
echo "update_due=false" >> "$GITHUB_OUTPUT"
exit 0
fi
SCHEDULED_DATE=$(cat update_date)
CURRENT_DATE=$(TZ="${{ env.TZ }}" date +%F)
echo "Scheduled Date: $SCHEDULED_DATE"
echo "Current Date: $CURRENT_DATE"
if [ "$SCHEDULED_DATE" != "$CURRENT_DATE" ]; then
echo "Update not due."
echo "update_due=false" >> "$GITHUB_OUTPUT"
exit 0
fi
echo "update_due=true" >> "$GITHUB_OUTPUT"
echo "scheduled_date=$SCHEDULED_DATE" >> "$GITHUB_OUTPUT"
update_branch:
needs: check_update
if: ${{ needs.check_update.outputs.update_due == 'true' }}
runs-on: ubuntu-latest
steps:
- name: Configure Git Identity
run: |
git config --global user.name "${{ github.actor }}"
git config --global user.email "${{ github.actor }}@users.noreply.github.com"
- name: Checkout ${{ env.BRANCH_STAGING }}
uses: actions/checkout@v3
with:
fetch-depth: 0
ref: ${{ env.BRANCH_STAGING }}
- name: Update README Date and Remove (${{ env.UPDATE_FILE }})
run: |
DAY=$(TZ="${{ env.TZ }}" date +'%d' | sed 's/^0//')
case "$DAY" in
1|21|31) SUFFIX="st" ;;
2|22) SUFFIX="nd" ;;
3|23) SUFFIX="rd" ;;
*) SUFFIX="th" ;;
esac
MONTH=$(TZ="${{ env.TZ }}" date +'%B')
YEAR=$(TZ="${{ env.TZ }}" date +'%Y')
NEW_DATE="**${MONTH} ${DAY}${SUFFIX}, ${YEAR}**"
echo "Updating README date to ${NEW_DATE}"
sed -i "/FrogPilot was last updated on:/ { N; N; s/\(\n\)\n.*$/\1\n${NEW_DATE}/; }" README.md
git add README.md
git rm -f "${{ env.UPDATE_FILE }}"
git commit -m "Updated README date to ${NEW_DATE}"
- name: Squash Commits
run: |
COMMIT_MSG=$(git log -1 --pretty=%B HEAD~1)
git reset --soft HEAD~2
git commit -m "$COMMIT_MSG"
- name: Rewrite Commit Dates to Noon ${{ env.TZ }}
run: |
COMMIT_DATETIME="${{ needs.check_update.outputs.scheduled_date }} 12:00"
COMMIT_PHX=$(TZ="${{ env.TZ }}" date -d "$COMMIT_DATETIME" +"%Y-%m-%dT%H:%M:%S %z")
git filter-branch --env-filter "export GIT_AUTHOR_DATE='$COMMIT_PHX'; export GIT_COMMITTER_DATE='$COMMIT_PHX'" "${{ env.BRANCH_STAGING }}"
- name: Fetch ${{ env.BRANCH_PREVIOUS }} and ${{ env.BRANCH_FROGPILOT }}
run: |
git fetch origin ${{ env.BRANCH_PREVIOUS }} ${{ env.BRANCH_FROGPILOT }}
- name: Wait Until Noon ${{ env.TZ }} Before Continuing
run: |
NOW=$(TZ="${{ env.TZ }}" date +%s)
TARGET=$(TZ="${{ env.TZ }}" date -d "12:00" +%s)
if [ "$NOW" -lt "$TARGET" ]; then
SLEEP_TIME=$((TARGET - NOW))
echo "Sleeping for $SLEEP_TIME seconds until noon..."
sleep $SLEEP_TIME
else
echo "It is already noon or past noon, proceeding immediately."
fi
- name: Push ${{ env.BRANCH_STAGING }}
run: |
git push origin "${{ env.BRANCH_STAGING }}" --force
- name: Reset ${{ env.BRANCH_PREVIOUS }} to Match ${{ env.BRANCH_FROGPILOT }}
run: |
git switch "${{ env.BRANCH_PREVIOUS }}"
git reset --hard "origin/${{ env.BRANCH_FROGPILOT }}"
git push origin "${{ env.BRANCH_PREVIOUS }}" --force
- name: Reset ${{ env.BRANCH_FROGPILOT }} to Match ${{ env.BRANCH_STAGING }}
run: |
git switch "${{ env.BRANCH_FROGPILOT }}"
git reset --hard "origin/${{ env.BRANCH_STAGING }}"
git push origin "${{ env.BRANCH_FROGPILOT }}" --force
+54 -12
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@@ -1,12 +1,20 @@
venv/
.venv/
.ci_cache
.env
.clang-format
.DS_Store
.tags
.ipynb_checkpoints
.idea
.overlay_init
.overlay_consistent
.sconsign.dblite
model2.png
a.out
.hypothesis
*.dylib
*.DSYM
*.d
*.pyc
@@ -14,30 +22,64 @@ a.out
.*.swp
.*.swo
.*.un~
*.tmp
*.o
*.o-*
*.os
*.os-*
*.so
*.a
*.clb
*.class
*.pyxbldc
*.vcd
*.qm
config.json
clcache
compile_commands.json
compare_runtime*.html
board/obj/
selfdrive/boardd/boardd
persist
selfdrive/pandad/pandad
cereal/services.h
cereal/gen
selfdrive/logcatd/logcatd
selfdrive/mapd/default_speeds_by_region.json
selfdrive/proclogd/proclogd
selfdrive/ui/ui
selfdrive/test/tests/plant/out
selfdrive/visiond/visiond
selfdrive/loggerd/loggerd
selfdrive/sensord/gpsd
selfdrive/sensord/sensord
system/proclogd/proclogd
selfdrive/ui/translations/alerts_generated.h
selfdrive/ui/translations/tmp
selfdrive/test/longitudinal_maneuvers/out
selfdrive/car/tests/cars_dump
system/camerad/camerad
system/camerad/test/ae_gray_test
selfdrive/modeld/_modeld
selfdrive/modeld/_dmonitoringmodeld
/src/
one
openpilot
xx
notebooks
hyperthneed
provisioning
.coverage*
coverage.xml
htmlcov
pandaextra
.mypy_cache/
flycheck_*
cppcheck_report.txt
comma*.sh
selfdrive/modeld/thneed/compile
selfdrive/modeld/models/*.thneed
selfdrive/modeld/models/*.pkl
*.bz2
build/
!**/.gitkeep
poetry.toml
Pipfile
+1
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@@ -0,0 +1 @@
3.11.4
-7
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@@ -1,7 +0,0 @@
sudo: required
services:
- docker
script:
- ./run_docker_tests.sh
-31
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@@ -1,31 +0,0 @@
# How to contribute
Our software is open source so you can solve your own problems without needing help from others. And if you solve a problem and are so kind, you can upstream it for the rest of the world to use.
Most open source development activity is coordinated through our [Discord](https://discord.comma.ai). A lot of documentation is available on our [medium](https://medium.com/@comma_ai/)
## Getting Started
* Join our [Discord](https://discord.comma.ai)
* Make sure you have a [GitHub account](https://github.com/signup/free)
* Fork [our repositories](https://github.com/commaai) on GitHub
## Testing
### Local Testing
You can test your changes on your machine by running `run_docker_tests.sh`. This will run some automated tests in docker against your code.
### Automated Testing
All PRs are automatically checked by travis. Check out `.travis.yml` for what travis runs. Any new tests sould be added to travis.
### Code Style and Linting
Code is automatically check for style by travis as part of the automated tests. You can also run these yourself by running `check_code_quality.sh`.
## Car Ports (openpilot)
We've released a [Model Port guide](https://medium.com/@comma_ai/openpilot-port-guide-for-toyota-models-e5467f4b5fe6) for porting to Toyota/Lexus models.
If you port openpilot to a substantially new car brand, see this more generic [Brand Port guide](https://medium.com/@comma_ai/how-to-write-a-car-port-for-openpilot-7ce0785eda84). You might also be eligible for a bounty. See our bounties at [comma.ai/bounties.html](https://comma.ai/bounties.html)
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FROM ubuntu:16.04
ENV PYTHONUNBUFFERED 1
RUN apt-get update && apt-get install -y \
autoconf \
build-essential \
bzip2 \
clang \
git \
libarchive-dev \
libavcodec-dev \
libavdevice-dev \
libavfilter-dev \
libavresample-dev \
libavutil-dev \
libffi-dev \
libglib2.0-0 \
libssl-dev \
libswscale-dev \
libtool \
libusb-1.0-0 \
libzmq5-dev \
ocl-icd-libopencl1 \
ocl-icd-opencl-dev \
opencl-headers \
pkg-config \
python-pip \
wget
COPY phonelibs/install_capnp.sh /tmp/install_capnp.sh
RUN /tmp/install_capnp.sh
RUN pip install --upgrade pip==18.0
RUN pip install pipenv==2018.11.26
COPY Pipfile /tmp/
COPY Pipfile.lock /tmp/
RUN cd /tmp && pipenv install --deploy --system
ENV PYTHONPATH /tmp/openpilot:$PYTHONPATH
RUN git clone --branch v0.6.2 https://github.com/commaai/openpilot-tools.git /tmp/openpilot/tools
RUN pip install -r /tmp/openpilot/tools/requirements.txt
RUN pip install fastcluster==1.1.20 scipy==0.19.1 dictdiffer==0.8.0 azure-batch==4.1.3 azure-common==1.1.16 azure-nspkg==3.0.0 azure-storage-blob==1.3.1 azure-storage-common==1.3.0 azure-storage-nspkg==3.0.0
COPY ./.pylintrc /tmp/openpilot/.pylintrc
COPY ./common /tmp/openpilot/common
COPY ./cereal /tmp/openpilot/cereal
COPY ./opendbc /tmp/openpilot/opendbc
COPY ./selfdrive /tmp/openpilot/selfdrive
COPY ./phonelibs /tmp/openpilot/phonelibs
COPY ./pyextra /tmp/openpilot/pyextra
COPY ./panda /tmp/openpilot/panda
RUN mkdir -p /tmp/openpilot/selfdrive/test/out
RUN make -C /tmp/openpilot/selfdrive/controls/lib/longitudinal_mpc clean
RUN make -C /tmp/openpilot/selfdrive/controls/lib/lateral_mpc clean
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code_dir := $(shell pwd)
# TODO: Add a global build system
.PHONY: all
all:
cd selfdrive && PYTHONPATH=$(code_dir) PREPAREONLY=1 ./manager.py
-145
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@@ -1,145 +0,0 @@
[[source]]
name = "pypi"
url = "https://pypi.org/simple"
verify_ssl = true
[dev-packages]
ipython = "<6.0"
aenum = "*"
azure-batch = "==4.1.3"
azure-common = "==1.1.16"
azure-nspkg = "==3.0.1"
azure-storage-blob = "==1.3.1"
azure-storage-common = "==1.3.0"
azure-storage-nspkg = "==3.0.0"
bincopy = "*"
bleach = "==1.5.0"
boto = "*"
"boto3" = "*"
celery = "*"
control = "*"
datadog = "*"
decorator = "*"
dlib = "*"
dominate = "*"
elasticsearch = "*"
entium = "==0.1.4"
fasteners = "*"
future = "*"
futures = "*"
gevent = "*"
pycocotools = {git = "https://github.com/cocodataset/cocoapi.git",subdirectory = "PythonAPI"}
gunicorn = "*"
"h5py" = "*"
hexdump = "*"
"html5lib" = "==0.9999999"
imageio = "*"
intervaltree = "*"
ipykernel = "<5.0"
joblib = "*"
json-logging-py = "*"
jupyter = "*"
libarchive = "*"
lru-dict = "*"
lxml = "*"
matplotlib = "==2.2.3"
"mpld3" = "*"
msgpack-python = "*"
nbstripout = "*"
nose-parameterized = "*"
numpy = "==1.14.5"
osmium = "==2.15.0"
pbr = "==5.1.3"
percache = "*"
pprofile = "*"
psutil = "*"
pycurl = "*"
git-pylint-commit-hook = "==2.5.1"
pymongo = "*"
"pynmea2" = "*"
pypolyline = "==0.1.17"
pysendfile = "*"
python-logstash = "*"
pyvcd = "*"
redis = "*"
redlock = "*"
"s2sphere" = "*"
scikit-image = "*"
"subprocess32" = "*"
supervisor = "*"
tenacity = "*"
tensorflow-gpu = "==1.13.0rc0"
"transforms3d" = "*"
utm = "*"
"v4l2" = "*"
visdom = "*"
PyJWT = "==1.4.1"
PyMySQL = "==0.9.2"
Theano = "*"
Werkzeug = "*"
"backports.lzma" = "*"
Flask-Cors = "*"
Flask-SocketIO = "*"
"GeoAlchemy2" = "*"
Keras = ">=2.1.6"
keras-maskrcnn = "*"
keras-retinanet = "*"
Pygments = "*"
PyNaCl = "*"
"PySDL2" = "*"
reverse_geocoder = "*"
Shapely = "*"
SQLAlchemy = "==1.2.7"
uWSGI = "*"
scipy = "*"
fastcluster = "==1.1.25"
backports-abc = "*"
pygame = "*"
simplejson = "*"
python-logstash-async = "*"
pandas = "*"
seaborn = "*"
tensorflow-estimator = "==1.10.12"
pyproj = "*"
[packages]
overpy = {git = "https://github.com/commaai/python-overpy.git",ref = "f86529af402d4642e1faeb146671c40284007323"}
atomicwrites = "*"
cffi = "*"
crcmod = "*"
hexdump = "*"
libusb1 = "*"
numpy = "*"
psutil = "*"
pycapnp = "*"
cryptography = "*"
pyserial = "*"
python-dateutil = "*"
pyzmq = "*"
raven = "*"
requests = "*"
setproctitle = "*"
six = "*"
smbus2 = "*"
sympy = "*"
tqdm = "*"
Cython = "*"
PyYAML = "*"
websocket_client = "*"
Logentries = {git = "https://github.com/commaai/le_python.git",ref = "5eef8f5be5929d33973e1b10e686fa0cdcd6792f"}
urllib3 = "*"
chardet = "*"
idna = "*"
gunicorn = "*"
utm = "*"
json-rpc = "*"
Flask = "*"
PyJWT = "*"
"Jinja2" = "*"
nose = "*"
pyflakes = "*"
pylint = "*"
pycryptodome = "*"
[requires]
python_version = "2.7"
Generated
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[![](https://i.imgur.com/UetIFyH.jpg)](#)
[![openpilot on the comma 3X](https://i.imgur.com/Zwv9SaN.png)](https://comma.ai/shop/comma-3x)
Welcome to openpilot
======
[openpilot](http://github.com/commaai/openpilot) is an open source driving agent. Currently, it performs the functions of Adaptive Cruise Control (ACC) and Lane Keeping Assist System (LKAS) for selected Honda, Toyota, Acura, Lexus, Chevrolet, Hyundai, Kia. It's about on par with Tesla Autopilot and GM Super Cruise, and better than [all other manufacturers](http://www.thedrive.com/tech/5707/the-war-for-autonomous-driving-part-iii-us-vs-germany-vs-japan).
The openpilot codebase has been written to be concise and to enable rapid prototyping. We look forward to your contributions - improving real vehicle automation has never been easier.
Table of Contents
=======================
* [Community](#community)
* [Hardware](#hardware)
* [Supported Cars](#supported-cars)
* [Community Maintained Cars](#community-maintained-cars)
* [In Progress Cars](#in-progress-cars)
* [How can I add support for my car?](#how-can-i-add-support-for-my-car)
* [Directory structure](#directory-structure)
* [User Data / chffr Account / Crash Reporting](#user-data--chffr-account--crash-reporting)
* [Testing on PC](#testing-on-pc)
* [Contributing](#contributing)
* [Licensing](#licensing)
---
Community
What is openpilot?
------
openpilot is developed by [comma.ai](https://comma.ai/) and users like you.
We have a [Twitter you should follow](https://twitter.com/comma_ai).
Also, we have a several thousand people community on [Discord](https://discord.comma.ai).
[openpilot](http://github.com/commaai/openpilot) is an open source driver assistance system. Currently, openpilot performs the functions of Adaptive Cruise Control (ACC), Automated Lane Centering (ALC), Forward Collision Warning (FCW), and Lane Departure Warning (LDW) for a growing variety of [supported car makes, models, and model years](docs/CARS.md). In addition, while openpilot is engaged, a camera-based Driver Monitoring (DM) feature alerts distracted and asleep drivers. See more about [the vehicle integration](docs/INTEGRATION.md) and [limitations](docs/LIMITATIONS.md).
<table>
<tr>
<td><a href="https://www.youtube.com/watch?v=ICOIin4p70w" title="YouTube" rel="noopener"><img src="https://i.imgur.com/gBTo7yB.png"></a></td>
<td><a href="https://www.youtube.com/watch?v=1zCtj3ckGFo" title="YouTube" rel="noopener"><img src="https://i.imgur.com/gNhhcep.png"></a></td>
<td><a href="https://www.youtube.com/watch?v=Qd2mjkBIRx0" title="YouTube" rel="noopener"><img src="https://i.imgur.com/tFnSexp.png"></a></td>
<td><a href="https://www.youtube.com/watch?v=ju12vlBm59E" title="YouTube" rel="noopener"><img src="https://i.imgur.com/3BKiJVy.png"></a></td>
</tr>
<tr>
<td><a href="https://www.youtube.com/watch?v=Z5VY5FzgNt4" title="YouTube" rel="noopener"><img src="https://i.imgur.com/3I9XOK2.png"></a></td>
<td><a href="https://www.youtube.com/watch?v=blnhZC7OmMg" title="YouTube" rel="noopener"><img src="https://i.imgur.com/f9IgX6s.png"></a></td>
<td><a href="https://www.youtube.com/watch?v=iRkz7FuJsA8" title="YouTube" rel="noopener"><img src="https://i.imgur.com/Vo5Zvmn.png"></a></td>
<td><a href="https://www.youtube.com/watch?v=IHjEqAKDqjM" title="YouTube" rel="noopener"><img src="https://i.imgur.com/V9Zd81n.png"></a></td>
<td><a href="https://youtu.be/NmBfgOanCyk" title="Video By Greer Viau"><img src="https://github.com/commaai/openpilot/assets/8762862/2f7112ae-f748-4f39-b617-fabd689c3772"></a></td>
<td><a href="https://youtu.be/VHKyqZ7t8Gw" title="Video By Logan LeGrand"><img src="https://github.com/commaai/openpilot/assets/8762862/92351544-2833-40d7-9e0b-7ef7ae37ec4c"></a></td>
<td><a href="https://youtu.be/SUIZYzxtMQs" title="A drive to Taco Bell"><img src="https://github.com/commaai/openpilot/assets/8762862/05ceefc5-2628-439c-a9b2-89ce77dc6f63"></a></td>
</tr>
</table>
Hardware
What is FrogPilot? 🐸
------
At the moment openpilot supports the [EON Dashcam DevKit](https://comma.ai/shop/products/eon-dashcam-devkit). A [panda](https://shop.comma.ai/products/panda-obd-ii-dongle) and a [giraffe](https://comma.ai/shop/products/giraffe/) are recommended tools to interface the EON with the car. We'd like to support other platforms as well.
FrogPilot is a fully open-sourced fork of openpilot, featuring clear and concise commits striving to be a resource for the openpilot developer community. It thrives on contributions from both users and developers, focusing on a collaborative, community-led approach to deliver an advanced openpilot experience for everyone!
Install openpilot on a neo device by entering ``https://openpilot.comma.ai`` during NEOS setup.
------
FrogPilot was last updated on:
Supported Cars
**April 12th, 2025**
Features
------
| Make | Model | Supported Package | Lateral | Longitudinal | No Accel Below | No Steer Below | Giraffe |
| ---------------------| -------------------------| ---------------------| --------| ---------------| -----------------| ---------------|-------------------|
| Acura | ILX 2016-18 | AcuraWatch Plus | Yes | Yes | 25mph<sup>1</sup>| 25mph | Nidec |
| Acura | RDX 2016-18 | AcuraWatch Plus | Yes | Yes | 25mph<sup>1</sup>| 12mph | Nidec |
| Buick<sup>3</sup> | Regal 2018 | Adaptive Cruise | Yes | Yes | 0mph | 7mph | Custom<sup>7</sup>|
| Chevrolet<sup>3</sup>| Malibu 2017 | Adaptive Cruise | Yes | Yes | 0mph | 7mph | Custom<sup>7</sup>|
| Chevrolet<sup>3</sup>| Volt 2017-18 | Adaptive Cruise | Yes | Yes | 0mph | 7mph | Custom<sup>7</sup>|
| Cadillac<sup>3</sup> | ATS 2018 | Adaptive Cruise | Yes | Yes | 0mph | 7mph | Custom<sup>7</sup>|
| Chrysler | Pacifica 2017-18 | Adaptive Cruise | Yes | Stock | 0mph | 9mph | FCA |
| Chrysler | Pacifica Hybrid 2017-18 | Adaptive Cruise | Yes | Stock | 0mph | 9mph | FCA |
| Chrysler | Pacifica Hybrid 2019 | Adaptive Cruise | Yes | Stock | 0mph | 39mph | FCA |
| GMC<sup>3</sup> | Acadia Denali 2018 | Adaptive Cruise | Yes | Yes | 0mph | 7mph | Custom<sup>7</sup>|
| Holden<sup>3</sup> | Astra 2017 | Adaptive Cruise | Yes | Yes | 0mph | 7mph | Custom<sup>7</sup>|
| Honda | Accord 2018-19 | All | Yes | Stock | 0mph | 3mph | Bosch |
| Honda | Accord Hybrid 2018-19 | All | Yes | Stock | 0mph | 3mph | Bosch |
| Honda | Civic Sedan/Coupe 2016-18| Honda Sensing | Yes | Yes | 0mph | 12mph | Nidec |
| Honda | Civic Sedan/Coupe 2019 | Honda Sensing | Yes | Stock | 0mph | 2mph | Bosch |
| Honda | Civic Hatchback 2017-19 | Honda Sensing | Yes | Stock | 0mph | 12mph | Bosch |
| Honda | CR-V 2015-16 | Touring | Yes | Yes | 25mph<sup>1</sup>| 12mph | Nidec |
| Honda | CR-V 2017-19 | Honda Sensing | Yes | Stock | 0mph | 12mph | Bosch |
| Honda | CR-V Hybrid 2017-2019 | Honda Sensing | Yes | Stock | 0mph | 12mph | Bosch |
| Honda | Odyssey 2018-19 | Honda Sensing | Yes | Yes | 25mph<sup>1</sup>| 0mph | Inverted Nidec |
| Honda | Passport 2019 | All | Yes | Yes | 25mph<sup>1</sup>| 12mph | Inverted Nidec |
| Honda | Pilot 2016-18 | Honda Sensing | Yes | Yes | 25mph<sup>1</sup>| 12mph | Nidec |
| Honda | Pilot 2019 | All | Yes | Yes | 25mph<sup>1</sup>| 12mph | Inverted Nidec |
| Honda | Ridgeline 2017-19 | Honda Sensing | Yes | Yes | 25mph<sup>1</sup>| 12mph | Nidec |
| Hyundai | Santa Fe 2019 | All | Yes | Stock | 0mph | 0mph | Custom<sup>6</sup>|
| Hyundai | Elantra 2017-19 | SCC + LKAS | Yes | Stock | 19mph | 34mph | Custom<sup>6</sup>|
| Hyundai | Genesis 2018 | All | Yes | Stock | 19mph | 34mph | Custom<sup>6</sup>|
| Jeep | Grand Cherokee 2016-18 | Adaptive Cruise | Yes | Stock | 0mph | 9mph | FCA |
| Jeep | Grand Cherokee 2019 | Adaptive Cruise | Yes | Stock | 0mph | 39mph | FCA |
| Kia | Optima 2019 | SCC + LKAS | Yes | Stock | 0mph | 0mph | Custom<sup>6</sup>|
| Kia | Sorento 2018 | All | Yes | Stock | 0mph | 0mph | Custom<sup>6</sup>|
| Kia | Stinger 2018 | SCC + LKAS | Yes | Stock | 0mph | 0mph | Custom<sup>6</sup>|
| Lexus | ES Hybrid 2019 | All | Yes | Yes | 0mph | 0mph | Toyota |
| Lexus | RX Hybrid 2016-19 | All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Subaru | Crosstrek 2018 | EyeSight | Yes | Stock | 0mph | 0mph | Custom<sup>4</sup>|
| Subaru | Impreza 2019 | EyeSight | Yes | Stock | 0mph | 0mph | Custom<sup>4</sup>|
| Toyota | Avalon 2016 | TSS-P | Yes | Yes<sup>2</sup>| 20mph<sup>1</sup>| 0mph | Toyota |
| Toyota | Avalon 2017-18 | All | Yes | Yes<sup>2</sup>| 20mph<sup>1</sup>| 0mph | Toyota |
| Toyota | Camry 2018-19 | All | Yes | Stock | 0mph<sup>5</sup> | 0mph | Toyota |
| Toyota | Camry Hybrid 2018-19 | All | Yes | Stock | 0mph<sup>5</sup> | 0mph | Toyota |
| Toyota | C-HR 2017-19 | All | Yes | Stock | 0mph | 0mph | Toyota |
| Toyota | C-HR Hybrid 2017-19 | All | Yes | Stock | 0mph | 0mph | Toyota |
| Toyota | Corolla 2017-19 | All | Yes | Yes<sup>2</sup>| 20mph<sup>1</sup>| 0mph | Toyota |
| Toyota | Corolla 2020 | All | Yes | Yes | 0mph | 0mph | Toyota |
| Toyota | Corolla Hatchback 2019 | All | Yes | Yes | 0mph | 0mph | Toyota |
| Toyota | Highlander 2017-19 | All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Highlander Hybrid 2017-19| All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Prius 2016 | TSS-P | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Prius 2017-19 | All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Prius Prime 2017-19 | All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Rav4 2016 | TSS-P | Yes | Yes<sup>2</sup>| 20mph<sup>1</sup>| 0mph | Toyota |
| Toyota | Rav4 2017-18 | All | Yes | Yes<sup>2</sup>| 20mph<sup>1</sup>| 0mph | Toyota |
| Toyota | Rav4 2019 | All | Yes | Yes | 0mph | 0mph | Toyota |
| Toyota | Rav4 Hybrid 2016 | TSS-P | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Rav4 Hybrid 2017-18 | All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
| Toyota | Sienna 2018 | All | Yes | Yes<sup>2</sup>| 0mph | 0mph | Toyota |
FrogPilot offers a wide range of customizable features that are easily toggled on or off to suit your preferences. Whether you want a completely stock openpilot experience, or want to add some fun and personal touches, FrogPilot has you covered! Some of the features include:
<sup>1</sup>[Comma Pedal](https://community.comma.ai/wiki/index.php/Comma_Pedal) is used to provide stop-and-go capability to some of the openpilot-supported cars that don't currently support stop-and-go. Here is how to [build a Comma Pedal](https://medium.com/@jfrux/comma-pedal-building-with-macrofab-6328bea791e8). ***NOTE: The Comma Pedal is not officially supported by [comma.ai](https://comma.ai).*** <br />
<sup>2</sup>When disconnecting the Driver Support Unit (DSU), otherwise longitudinal control is stock ACC. For DSU locations, see [Toyota Wiki page](https://community.comma.ai/wiki/index.php/Toyota). <br />
<sup>3</sup>[GM installation guide](https://zoneos.com/volt/). <br />
<sup>4</sup>Subaru Giraffe is DIY. <br />
<sup>5</sup>28mph for Camry 4CYL L, 4CYL LE and 4CYL SE which don't have Full-Speed Range Dynamic Radar Cruise Control. <br />
<sup>6</sup>Open sourced [Hyundai Giraffe](https://github.com/commaai/neo/tree/master/giraffe/hyundai) is designed for the 2019 Sante Fe; pinout may differ for other Hyundais. <br />
<sup>7</sup>Community built Giraffe, find more information [here](https://zoneos.com/shop/). <br />
------
**Advanced Customizations:**
Community Maintained Cars
- "Alert Volume Controller" to set the volume level for each of of openpilot's sounds
- Customize the following distance and jerk values for each personality profile
- Fine tune your car's "Steer Ratio" to perfect your lateral control
- Increase the distance when stopped behind lead vehicles
- Increase the max set speed by a custom interval (i.e. 2, 3, 4, 5, 6, etc. instead of just 1)
- Select between past, present, and future openpilot driving models
------
🎨 **Custom Themes:**
- 🐸 Frog theme (with a bonus 🐐 sound effect)
- <img src="https://images.emojiterra.com/google/noto-emoji/unicode-15/color/512px/1f1f7-1f1fa.png" width="15" height="12"> Russia / Joseph Stalin theme
- 🔌 Tesla theme
- Holiday themes! Minor holidays last a day, while major holidays (Easter, Halloween, Thanksgiving, Christmas) last a week
- Random events triggered by specific actions while driving with openpilot
- 📢 Want to add a theme? Request one in the "feature-request" channel in the FrogPilot Discord!
------
🚀 **Conditional Experimental Mode:**
- Auto-activates "Experimental Mode" under several conditions, including:
- Approaching intersections and turns while using navigation
- Approaching slower vehicles to take advantage of "Experimental Mode"'s smoother braking
- Curve and stop light/stop sign detection
- Driving below a set speed
- Turn signal activation below 55mph for turn assistance
------
📊 **Developer UI:**
- Display various driving logics such as the distance, speed, and the desired following distance to your lead vehicle
- Lane measuring of the adjacent lanes for lane detection
- Tap the "VEHICLE ONLINE"/"CPU"/"GPU" gauge to toggle between CPU and GPU monitoring
- Tap the "CONNECT ONLINE"/"MEMORY"/"LEFT"/"USED" gauge to toggle between RAM and storage monitoring
------
🛠 **Device Management:**
- Adjustable screen brightness for both onroad and offroad states
- Adjustable screen timeout times for both onroad and offroad states
- Backup and restore previous versions of FrogPilot
- Backup and restore previous versions of toggle configurations
- Battery level threshold to automatically shut the device down after you car's battery falls below a set voltage limit when offroad
- Delete stored driving data for increased privacy/space via the "Device" panel
- Device can operate offline indefinitely
- Disable logging and/or uploading
- Disable uploads while onroad to help reduce data usage
- Flash the Panda within the "Device" menu
- "Standby Mode" that wakes the screen up between engagement states or when important alerts are triggered
- Timer to automatically shut down after going offroad
------
🚖 **Lateral Adjustments:**
- Activate lateral control by simply pressing the "Cruise Control" button
- Force comma's auto tuning for unsupported vehicles
- Lateral control won't disengage on gas or brake
- Nudgeless lane changes with lane detection to prevent driving into curbs or going offroad
- Pause lateral control when below a set speed
- Pause lateral control when pressing the brake
- Pause lateral control when turn signals are active
- Precise turns by using turn desires when below the minimum lane change speed
- [Twilsonco's NNFF](https://github.com/twilsonco/openpilot) for smoother steering control
------
🚘 **Longitudinal Adjustments:**
- Aggressive acceleration when following a lead vehicle from a stop
- "Map Turn Speed Controller" to slow down for curves based on stored map data
- With an additional toggle to fine tune the speed aggressiveness
- Smoother braking behind slower leads
- "Speed Limit Controller" to adjust your speed to the posted speed limit
- With additional toggles to set offsets for "0-34 mph", "35-54 mph", "55-64 mph", and "65-99 mph"
- "Sport" and "Eco" acceleration and deceleration profiles
- "Traffic Mode" tailored towards driving in traffic
- Tweak the lead detection threshold to detect leads sooner for smoother braking on stopped/slower vehicles
- "Vision Turn Speed Controller" for smoother handling of curves
- With additional toggles to fine tune the speed aggressiveness and curve detection sensitivity
------
🗺️ **Navigation:**
- 3D buildings
- Custom map styles
- Full screen map that takes up the entire screen for a more expansive map view
- iOS shortcuts to quickly set navigation destinations
- Navigate on openpilot without a comma prime subscription
- Offline maps
- "Open Street Maps" integration for speed limit control and road name view
------
🎮 **Onroad UI:**
- Compass that rotates according to the direction you're driving
- FPS counter in the screen's border
- Hide various UI elements on the screen for a cleaner UI
- Pedals on the onroad UI indicate when the gas/brake pedals are being used
- Road UI Customizations:
- Acceleration path to show the model's desired acceleration/deceleration
- Blind spot path when a vehicle is detected in your blind spot
- Increase/decrease the lane line, path, and road edge widths
- Path edge colors based on specific driving statuses:
- 🔵 Blue - Navigation active
- 🟦 Light Blue - "Always On Lateral" active
- 🟢 Green - Default
- 🟠 Orange - "Experimental Mode" active
- 🔴 Red - "Traffic Mode" active
- 🟡 Yellow - "Conditional Experimental Mode" overridden
- "Unlimited" road UI that extends out as far as the model can see
- Sidebar retains it's previous position between reboots/ignition cycles
- Steering wheel icons
- 📢 Request your own steering wheel icon in the "feature-request" channel!
- Steering wheel in the onroad UI rotates alongside your physical steering wheel
------
🚙 **Vehicle Specific Additions:**
- Automatic/manual fingerprint selection to force a selected fingerprint
- Custom longitudinal tunings for GM EVs and trucks for smoother gas and brake control
- Custom longitudinal tunings for Toyota/Lexus for smoother gas and brake control
- Disable openpilot longitudinal control to use your car's stock cruise control
- GM Volt support
- Honda Clarity support
- Increased torque for the Subaru Crosstrek
- Lock doors automatically when in the drive gear for Toyota/Lexus and unlock when in park
- openpilot longitudinal control for GM vehicles without ACC
- Pedal interceptor support for GM vehicles
- "Stop and Go" hack for Toyota's without stop and go functionality
- ZSS support for the Toyota Prius and Sienna
------
🚦 **Quality of Life Features:**
- Automatic updates for a completely "set and forget" experience
- Camera view selection
- Custom alerts for green lights, vehicles in blindspot, lead departing, and the current speed limit changing
- Display the driver camera when in reverse
- Driving statistics to show how many hours and miles you've driven on the home screen
- Fleet Manager to easily access your driving data and screen recordings
- Numerical temperature gauge
- Retain tethering status between reboots
- Screenrecorder
- Toggle "Experimental Mode" via the "Lane Departure Alert" button, holding down the "Distance" button for 0.5+ seconds, or by double tapping the screen
How to Install
------
| Make | Model | Supported Package | Lateral | Longitudinal | No Accel Below | No Steer Below | Giraffe |
| ---------------------| -------------------------| ---------------------| --------| ---------------| -----------------| ---------------|-------------------|
| Honda | Fit 2018 | Honda Sensing | Yes | Yes | 25mph<sup>1</sup>| 12mph | Inverted Nidec |
| Tesla | Model S 2012-13 | All | Yes | Not yet | Not applicable | 0mph | Custom<sup>8</sup>|
Easiest way to install FrogPilot is via this URL at the installation screen:
[[Honda Fit Pull Request]](https://github.com/commaai/openpilot/pull/266). <br />
[[Tesla Model S Pull Request]](https://github.com/commaai/openpilot/pull/246) <br />
<sup>8</sup>Community built Giraffe, find more information here [Community Tesla Giraffe](https://github.com/jeankalud/neo/tree/tesla_giraffe/giraffe/tesla) <br />
Community Maintained Cars are not confirmed by comma.ai to meet our [safety model](https://github.com/commaai/openpilot/blob/devel/SAFETY.md). Be extra cautious using them.
In Progress Cars
------
- All TSS-P Toyota with Steering Assist and LSS-P Lexus with Steering Assist or Lane Keep Assist.
- Only remaining Toyota cars with no port yet are the Avalon and the Sienna.
- All Hyundai with SmartSense.
- All Kia with SCC and LKAS.
- All Chrysler, Jeep, Fiat with Adaptive Cruise Control and LaneSense.
How can I add support for my car?
------
If your car has adaptive cruise control and lane keep assist, you are in luck. Using a [panda](https://comma.ai/shop/products/panda-obd-ii-dongle/) and [cabana](https://community.comma.ai/cabana/), you can understand how to make your car drive by wire.
We've written guides for [Brand](https://medium.com/@comma_ai/how-to-write-a-car-port-for-openpilot-7ce0785eda84) and [Model](https://medium.com/@comma_ai/openpilot-port-guide-for-toyota-models-e5467f4b5fe6) ports. These guides might help you after you have the basics figured out.
- BMW, Audi, Volvo, and Mercedes all use [FlexRay](https://en.wikipedia.org/wiki/FlexRay) and can be supported after [FlexRay support](https://github.com/commaai/openpilot/pull/463) is merged.
- We put time into a Ford port, but the steering has a 10 second cutout limitation that makes it unusable.
- The 2016-2017 Honda Accord uses a custom signaling protocol for steering that's unlikely to ever be upstreamed.
Directory structure
------
.
├── apk # The apk files used for the UI
├── cereal # The messaging spec used for all logs on EON
├── common # Library like functionality we've developed here
├── installer/updater # Manages auto-updates of openpilot
├── opendbc # Files showing how to interpret data from cars
├── panda # Code used to communicate on CAN and LIN
├── phonelibs # Libraries used on EON
├── pyextra # Libraries used on EON
└── selfdrive # Code needed to drive the car
├── assets # Fonts and images for UI
├── athena # Allows communication with the app
├── boardd # Daemon to talk to the board
├── can # Helpers for parsing CAN messages
├── car # Car specific code to read states and control actuators
├── common # Shared C/C++ code for the daemons
├── controls # Perception, planning and controls
├── debug # Tools to help you debug and do car ports
├── locationd # Soon to be home of precise location
├── logcatd # Android logcat as a service
├── loggerd # Logger and uploader of car data
├── proclogd # Logs information from proc
├── sensord # IMU / GPS interface code
├── test # Car simulator running code through virtual maneuvers
├── ui # The UI
└── visiond # Vision pipeline
To understand how the services interact, see `selfdrive/service_list.yaml`
User Data / chffr Account / Crash Reporting
------
By default, openpilot creates an account and includes a client for chffr, our dashcam app. We use your data to train better models and improve openpilot for everyone.
It's open source software, so you are free to disable it if you wish.
It logs the road facing camera, CAN, GPS, IMU, magnetometer, thermal sensors, crashes, and operating system logs.
The user facing camera is only logged if you explicitly opt-in in settings.
It does not log the microphone.
By using it, you agree to [our privacy policy](https://community.comma.ai/privacy.html). You understand that use of this software or its related services will generate certain types of user data, which may be logged and stored at the sole discretion of comma.ai. By accepting this agreement, you grant an irrevocable, perpetual, worldwide right to comma.ai for the use of this data.
Testing on PC
------
Check out [openpilot-tools](https://github.com/commaai/openpilot-tools): lots of tools you can use to replay driving data, test and develop openpilot from your pc.
Also, within openpilot there is a rudimentary infrastructure to run a basic simulation and generate a report of openpilot's behavior in different longitudinal control scenarios.
```bash
# Requires working docker
./run_docker_tests.sh
```
frogpilot.download
```
Contributing
DO NOT install the "FrogPilot-Development" branch. I'm constantly breaking things on there, so unless you don't want to use openpilot, NEVER install it!
![](https://i.imgur.com/swr0kqJ.png)
Bug reports / Feature Requests
------
We welcome both pull requests and issues on [github](http://github.com/commaai/openpilot). Bug fixes and new car ports encouraged.
If you encounter any issues or bugs while using FrogPilot, or if you have any suggestions for new features or improvements, please don't hesitate to post about it on the Discord! I'm always looking for ways to improve the fork and provide a better experience for everyone!
We also have a [bounty program](https://comma.ai/bounties.html).
To report a bug or request a new feature, make a post in the #bug-reports or #feature-requests channel respectively on the FrogPilot Discord. Please provide as much detail as possible about the issue you're experiencing or the feature you'd like to see added. Photos, videos, log files, or other relevant information are very helpful!
Want to get paid to work on openpilot? [comma.ai is hiring](https://comma.ai/jobs/)
I will do my best to respond to bug reports and feature requests in a timely manner, but please understand that I may not be able to address every request immediately. Your feedback and suggestions are valuable, and I appreciate your help in making FrogPilot the best it can be!
Discord
------
[Join the FrogPilot Community Discord!](https://discord.gg/frogpilot)
Credits
------
* [AlexandreSato](https://github.com/AlexandreSato)
* [Crwusiz](https://github.com/crwusiz)
* [DragonPilot](https://github.com/dragonpilot-community)
* [ErichMoraga](https://github.com/ErichMoraga)
* [Garrettpall](https://github.com/garrettpall)
* [Mike8643](https://github.com/mike8643)
* [Neokii](https://github.com/Neokii)
* [OPGM](https://github.com/opgm)
* [OPKR](https://github.com/openpilotkr)
* [Pfeiferj](https://github.com/pfeiferj)
* [ServerDummy](https://github.com/ServerDummy)
* [Twilsonco](https://github.com/twilsonco)
Licensing
------
@@ -237,3 +223,6 @@ NO WARRANTY EXPRESSED OR IMPLIED.**
---
<img src="https://d1qb2nb5cznatu.cloudfront.net/startups/i/1061157-bc7e9bf3b246ece7322e6ffe653f6af8-medium_jpg.jpg?buster=1458363130" width="75"></img> <img src="https://cdn-images-1.medium.com/max/1600/1*C87EjxGeMPrkTuVRVWVg4w.png" width="225"></img>
![openpilot tests](https://github.com/commaai/openpilot/actions/workflows/selfdrive_tests.yaml/badge.svg)
[![codecov](https://codecov.io/gh/commaai/openpilot/branch/master/graph/badge.svg)](https://codecov.io/gh/commaai/openpilot)
-36
View File
@@ -1,36 +0,0 @@
Welcome to chffrplus
======
[chffrplus](https://github.com/commaai/chffrplus) is an open source dashcam.
This is the shipping reference software for the comma EON Dashcam DevKit. It keeps many of the niceities of [openpilot](https://github.com/commaai/openpilot), like high quality sensors, great camera, and good autostart and stop. Though unlike openpilot, it cannot control your car. chffrplus can interface with your car through a [panda](https://shop.comma.ai/products/panda-obd-ii-dongle), but just like our dashcam app [chffr](https://getchffr.com/), it is read only.
It integrates with the rest of the comma ecosystem, so you can view your drives on the [chffr](https://getchffr.com/) app for Android or iOS, and reverse engineer your car with [cabana](https://community.comma.ai/cabana/?demo=1).
Hardware
------
Right now chffrplus supports the [EON Dashcam DevKit](https://shop.comma.ai/products/eon-dashcam-devkit) for hardware to run on.
Install chffrplus on a EON device by entering ``https://chffrplus.comma.ai`` during NEOS setup.
User Data / chffr Account / Crash Reporting
------
By default chffrplus creates an account and includes a client for chffr, our dashcam app.
It's open source software, so you are free to disable it if you wish.
It logs the road facing camera, CAN, GPS, IMU, magnetometer, thermal sensors, crashes, and operating system logs.
It does not log the user facing camera or the microphone.
By using it, you agree to [our privacy policy](https://beta.comma.ai/privacy.html). You understand that use of this software or its related services will generate certain types of user data, which may be logged and stored at the sole discretion of comma.ai. By accepting this agreement, you grant an irrevocable, perpetual, worldwide right to comma.ai for the use of this data.
Licensing
------
chffrplus is released under the MIT license.
+644 -53
View File
@@ -1,3 +1,594 @@
Version 0.9.7 (2024-06-13)
========================
* New driving model
* Inputs the past curvature for smoother and more accurate lateral control
* Simplified neural network architecture in the model's last layers
* Minor fixes to desire augmentation and weight decay
* New driver monitoring model
* Improved end-to-end bit for phone detection
* Adjust driving personality with the follow distance button
* Support for hybrid variants of supported Ford models
* Fingerprinting without the OBD-II port on all cars
* Improved fuzzy fingerprinting for Ford and Volkswagen
Version 0.9.6 (2024-02-27)
========================
* New driving model
* Vision model trained on more data
* Improved driving performance
* Directly outputs curvature for lateral control
* New driver monitoring model
* Trained on larger dataset
* Model path UI
* Shows where driving model wants to be
* Shows what model is seeing more clearly, but more jittery
* AGNOS 9
* comma body streaming and controls over WebRTC
* Improved fuzzy fingerprinting for many makes and models
* Alpha longitudinal support for new Toyota models
* Chevrolet Equinox 2019-22 support thanks to JasonJShuler and nworb-cire!
* Dodge Durango 2020-21 support
* Hyundai Staria 2023 support thanks to sunnyhaibin!
* Kia Niro Plug-in Hybrid 2022 support thanks to sunnyhaibin!
* Lexus LC 2024 support thanks to nelsonjchen!
* Toyota RAV4 2023-24 support
* Toyota RAV4 Hybrid 2023-24 support
Version 0.9.5 (2023-11-17)
========================
* New driving model
* Improved navigate on openpilot performance using navigation instructions as an additional model input
* Do lateral planning inside the model
* New vision transformer architecture
* Cadillac Escalade ESV 2019 support thanks to twilsonco!
* Hyundai Azera 2022 support thanks to sunnyhaibin!
* Hyundai Azera Hybrid 2020 support thanks to chanhojung and haram-KONA!
* Hyundai Custin 2023 support thanks to sunnyhaibin and Saber422!
* Hyundai Ioniq 6 2023 support thanks to sunnyhaibin and alamo3!
* Hyundai Kona Electric 2023 (Korean version) support thanks to sunnyhaibin and haram-KONA!
* Kia K8 Hybrid (with HDA II) 2023 support thanks to sunnyhaibin!
* Kia Optima Hybrid 2019 support
* Kia Sorento Hybrid 2023 support thanks to sunnyhaibin!
* Lexus GS F 2016 support thanks to snyperifle!
* Lexus IS 2023 support thanks to L3R5!
Version 0.9.4 (2023-07-27)
========================
* comma 3X support
* Navigate on openpilot in Experimental mode
* When navigation has a destination, openpilot will input the map information into the model, which provides useful context to help the model understand the scene
* When navigating on openpilot, openpilot will keep left or right appropriately at forks and exits
* When navigating on openpilot, lane change behavior is unchanged and still activated by the driver
* When navigate on openpilot is active, the path on the map is green
* UI updates
* Navigation settings moved to home screen and map
* Border color always shows engagement status. Blue means disengaged, green means engaged, and grey means engaged with human overriding
* Alerts are shown inside the border. Black means info, orange means warning, and red means critical alert
* Bookmarked segments are preserved on the device's storage
* Ford Focus 2018 support
* Kia Carnival 2023 support thanks to sunnyhaibin!
Version 0.9.3 (2023-06-29)
========================
* New driving model
* Improved height estimation and added height tracking in liveCalibration
* Model inputs refactor
* New driving personality setting
* Three settings: aggressive, standard, and relaxed
* Standard is recommended and the default
* In aggressive mode, lead follow distance is shorter and acceleration response is quicker
* In relaxed mode, lead follow distance is longer
* Improved fuzzy fingerprinting for Hyundai, Kia, and Genesis
* Improved thermal management logic
Version 0.9.2 (2023-05-22)
========================
* New driving model
* Reduced turn diving
* Trained on a new dataset
* UI updates
* New experimental mode visualization
* Draw MPC path instead of model-predicted path
* AGNOS 7
* Faster boot time
* Fixes rare no sounds bug
* Fixes bootsplash bug at extreme temperatures
* Buick LaCrosse 2017-19 support thanks to koch-cf!
* Chevrolet Trailblazer 2021-22 support thanks to TurboCE!
* Ford Bronco Sport 2021-22 support
* Ford Escape 2020-22 support
* Ford Explorer 2020-22 support
* Ford Kuga 2020-22 support
* Ford Maverick 2022-23 support
* Genesis GV80 2023 support thanks to JWingate80!
* Honda HR-V 2023 support thanks to AlexandreSato and galegozi!
* Kia Niro EV 2023 support thanks to JosselinLecocq!
* Lexus ES 2017-18 support
* Lincoln Aviator 2021 support
* Škoda Fabia 2022-23 support thanks to jyoung8607!
Version 0.9.1 (2023-02-28)
========================
* New driving model
* 30% improved height estimation resulting in better driving performance for tall cars
* Driver monitoring: removed timer resetting on user interaction if distracted
* UI updates
* Adjust alert volume using ambient noise level
* Driver monitoring icon shows driver's head pose
* German translation thanks to Vrabetz and CzokNorris!
* Cadillac Escalade 2017 support thanks to rickygilleland!
* Chevrolet Bolt EV 2022-23 support thanks to JasonJShuler!
* Genesis GV60 2023 support thanks to sunnyhaibin!
* Hyundai Tucson 2022-23 support
* Kia K5 Hybrid 2020 support thanks to sunnyhaibin!
* Kia Niro Hybrid 2023 support thanks to sunnyhaibin!
* Kia Sorento 2022-23 support thanks to sunnyhaibin!
* Kia Sorento Plug-in Hybrid 2022 support thanks to sunnyhaibin!
* Toyota C-HR 2021 support thanks to eFiniLan!
* Toyota C-HR Hybrid 2022 support thanks to Korben00!
* Volkswagen Crafter and MAN TGE 2017-23 support thanks to jyoung8607!
Version 0.9.0 (2022-11-21)
========================
* New driving model
* Internal feature space information content increased tenfold during training to ~700 bits, which makes the model dramatically more accurate
* Less reliance on previous frames makes model more reactive and snappy
* Trained in new reprojective simulator
* Trained in 36 hours from scratch, compared to one week for previous releases
* Training now simulates both lateral and longitudinal behavior, which allows openpilot to slow down for turns, stop at traffic lights, and more in experimental mode
* Experimental driving mode
* End-to-end longitudinal control
* Stops for traffic lights and stop signs
* Slows down for turns
* openpilot defaults to chill mode, enable experimental mode in settings
* Driver monitoring updates
* New bigger model with added end-to-end distracted trigger
* Reduced false positives during driver calibration
* Self-tuning torque controller: learns parameters live for each car
* Torque controller used on all Toyota, Lexus, Hyundai, Kia, and Genesis models
* UI updates
* Matched speeds shown on car's dash
* Multi-language in navigation
* Improved update experience
* Border turns grey while overriding steering
* Bookmark events while driving; view them in comma connect
* New onroad visualization for experimental mode
* tools: new and improved cabana thanks to deanlee!
* Experimental longitudinal support for Volkswagen, CAN-FD Hyundai, and new GM models
* Genesis GV70 2022-23 support thanks to zunichky and sunnyhaibin!
* Hyundai Santa Cruz 2021-22 support thanks to sunnyhaibin!
* Kia Sportage 2023 support thanks to sunnyhaibin!
* Kia Sportage Hybrid 2023 support thanks to sunnyhaibin!
* Kia Stinger 2022 support thanks to sunnyhaibin!
Version 0.8.16 (2022-08-26)
========================
* New driving model
* Reduced turn cutting
* Auto-detect right hand drive setting with driver monitoring model
* Improved fan controller for comma three
* New translations
* Japanese thanks to cydia2020!
* Brazilian Portuguese thanks to AlexandreSato!
* Chevrolet Bolt EUV 2022-23 support thanks to JasonJShuler!
* Chevrolet Silverado 1500 2020-21 support thanks to JasonJShuler!
* GMC Sierra 1500 2020-21 support thanks to JasonJShuler!
* Hyundai Ioniq 5 2022 support thanks to sunnyhaibin!
* Hyundai Kona Electric 2022 support thanks to sunnyhaibin!
* Hyundai Tucson Hybrid 2022 support thanks to sunnyhaibin!
* Subaru Legacy 2020-22 support thanks to martinl!
* Subaru Outback 2020-22 support
Version 0.8.15 (2022-07-20)
========================
* New driving model
* Path planning uses end-to-end output instead of lane lines at all times
* Reduced ping pong
* Improved lane centering
* New lateral controller based on physical wheel torque model
* Much smoother control that's consistent across the speed range
* Effective feedforward that uses road roll
* Simplified tuning, all car-specific parameters can be derived from data
* Used on select Toyota and Hyundai models at first
* Significantly improved control on TSS-P Prius
* New driver monitoring model
* Bigger model, covering full interior view from driver camera
* Works with a wider variety of mounting angles
* 3x more unique comma three training data than previous
* Navigation improvements
* Speed limits shown while navigating
* Faster position fix by using raw GPS measurements
* UI updates
* Multilanguage support for settings and home screen
* New font
* Refreshed max speed design
* More consistent camera view perspective across cars
* Reduced power usage: device runs cooler and fan spins less
* AGNOS 5
* Support VSCode remote SSH target
* Support for delta updates to reduce data usage on future OS updates
* Chrysler ECU firmware fingerprinting thanks to realfast!
* Honda Civic 2022 support
* Hyundai Tucson 2021 support thanks to bluesforte!
* Kia EV6 2022 support
* Lexus NX Hybrid 2020 support thanks to AlexandreSato!
* Ram 1500 2019-21 support thanks to realfast!
Version 0.8.14 (2022-06-01)
========================
* New driving model
* Bigger model, using both of comma three's road-facing cameras
* Better at cut-in detection and tight turns
* New driver monitoring model
* Tweaked network structure to improve output resolution for DSP
* Fixed bug in quantization aware training to reduce quantizing errors
* Resulted in 7x less MSE and no more random biases at runtime
* Added toggle to disable disengaging on the accelerator pedal
* comma body support
* Audi RS3 support thanks to jyoung8607!
* Hyundai Ioniq Plug-in Hybrid 2019 support thanks to sunnyhaibin!
* Hyundai Tucson Diesel 2019 support thanks to sunnyhaibin!
* Toyota Alphard Hybrid 2021 support
* Toyota Avalon Hybrid 2022 support
* Toyota RAV4 2022 support
* Toyota RAV4 Hybrid 2022 support
Version 0.8.13 (2022-02-18)
========================
* Improved driver monitoring
* Re-tuned driver pose learner for relaxed driving positions
* Added reliance on driving model to be more scene adaptive
* Matched strictness between comma two and comma three
* Improved performance in turns by compensating for the road bank angle
* Improved camera focus on the comma two
* AGNOS 4
* ADB support
* improved cell auto configuration
* NEOS 19
* package updates
* stability improvements
* Subaru ECU firmware fingerprinting thanks to martinl!
* Hyundai Santa Fe Plug-in Hybrid 2022 support thanks to sunnyhaibin!
* Mazda CX-5 2022 support thanks to Jafaral!
* Subaru Impreza 2020 support thanks to martinl!
* Toyota Avalon 2022 support thanks to sshane!
* Toyota Prius v 2017 support thanks to CT921!
* Volkswagen Caravelle 2020 support thanks to jyoung8607!
Version 0.8.12 (2021-12-15)
========================
* New driving model
* Improved behavior around exits
* Better pose accuracy at high speeds, allowing max speed of 90mph
* Fully incorporated comma three data into all parts of training stack
* Improved follow distance
* Better longitudinal policy, especially in low speed traffic
* New alert sounds
* AGNOS 3
* Display burn in mitigation
* Improved audio amplifier configuration
* System reliability improvements
* Update Python to 3.8.10
* Raw logs upload moved to connect.comma.ai
* Fixed HUD alerts on newer Honda Bosch thanks to csouers!
* Audi Q3 2020-21 support thanks to jyoung8607!
* Lexus RC 2020 support thanks to ErichMoraga!
Version 0.8.11 (2021-11-29)
========================
* Support for CAN FD on the red panda
* Support for an external panda on the comma three
* Navigation: Show more detailed instructions when approaching maneuver
* Fixed occasional steering faults on GM cars thanks to jyoung8607!
* Nissan ECU firmware fingerprinting thanks to robin-reckmann, martinl, and razem-io!
* Cadillac Escalade ESV 2016 support thanks to Gibby!
* Genesis G70 2020 support thanks to tecandrew!
* Hyundai Santa Fe Hybrid 2022 support thanks to sunnyhaibin!
* Mazda CX-9 2021 support thanks to Jacar!
* Volkswagen Polo 2020 support thanks to jyoung8607!
* Volkswagen T-Roc 2021 support thanks to jyoung8607!
Version 0.8.10 (2021-11-01)
========================
* New driving model
* Trained on one million minutes!!!
* Fixed lead training making lead predictions significantly more accurate
* Fixed several localizer dataset bugs and loss function bugs, overall improved accuracy
* New driver monitoring model
* Trained on latest data from both comma two and comma three
* Increased model field of view by 40% on comma three
* Improved model stability on masked users
* Improved pose prediction with reworked ground-truth stack
* Lateral and longitudinal planning MPCs now in ACADOS
* Combined longitudinal MPCs
* All longitudinal planning now happens in a single MPC system
* Fixed instability in MPC problem to prevent sporadic CPU usage
* AGNOS 2: minor stability improvements and builder repo open sourced
* tools: new and improved replay thanks to deanlee!
* Moved community-supported cars outside of the Community Features toggle
* Improved FW fingerprinting reliability for Hyundai/Kia/Genesis
* Added prerequisites for longitudinal control on Hyundai/Kia/Genesis and Honda Bosch
* Audi S3 2015 support thanks to jyoung8607!
* Honda Freed 2020 support thanks to belm0!
* Hyundai Ioniq Hybrid 2020-2022 support thanks to sunnyhaibin!
* Hyundai Santa Fe 2022 support thanks to sunnyhaibin!
* Kia K5 2021 support thanks to sunnyhaibin!
* Škoda Kamiq 2021 support thanks to jyoung8607!
* Škoda Karoq 2019 support thanks to jyoung8607!
* Volkswagen Arteon 2021 support thanks to jyoung8607!
* Volkswagen California 2021 support thanks to jyoung8607!
* Volkswagen Taos 2022 support thanks to jyoung8607!
Version 0.8.9 (2021-09-14)
========================
* Improved fan control on comma three
* AGNOS 1.5: improved stability
* Honda e 2020 support
Version 0.8.8 (2021-08-27)
========================
* New driving model with improved laneless performance
* Trained on 5000+ hours of diverse driving data from 3000+ users in 40+ countries
* Better anti-cheating methods during simulator training ensure the model hugs less when in laneless mode
* All new desire ground-truthing stack makes the model better at lane changes
* New driver monitoring model: improved performance on comma three
* NEOS 18 for comma two: update packages
* AGNOS 1.3 for comma three: fix display init at high temperatures
* Improved auto-exposure on comma three
* Improved longitudinal control on Honda Nidec cars
* Hyundai Kona Hybrid 2020 support thanks to haram-KONA!
* Hyundai Sonata Hybrid 2021 support thanks to Matt-Wash-Burn!
* Kia Niro Hybrid 2021 support thanks to tetious!
Version 0.8.7 (2021-07-31)
========================
* comma three support!
* Navigation alpha for the comma three!
* Volkswagen T-Cross 2021 support thanks to jyoung8607!
Version 0.8.6 (2021-07-21)
========================
* Revamp lateral and longitudinal planners
* Refactor planner output API to be more readable and verbose
* Planners now output desired trajectories for speed, acceleration, curvature, and curvature rate
* Use MPC for longitudinal planning when no lead car is present, makes accel and decel smoother
* Remove "CHECK DRIVER FACE VISIBILITY" warning
* Fixed cruise fault on some TSS2.5 Camrys and international Toyotas
* Hyundai Elantra Hybrid 2021 support thanks to tecandrew!
* Hyundai Ioniq PHEV 2020 support thanks to YawWashout!
* Kia Niro Hybrid 2019 support thanks to jyoung8607!
* Škoda Octavia RS 2016 support thanks to jyoung8607!
* Toyota Alphard 2020 support thanks to belm0!
* Volkswagen Golf SportWagen 2015 support thanks to jona96!
* Volkswagen Touran 2017 support thanks to jyoung8607!
Version 0.8.5 (2021-06-11)
========================
* NEOS update: improved reliability and stability with better voltage regulator configuration
* Smart model-based Forward Collision Warning
* CAN-based fingerprinting moved behind community features toggle
* Improved longitudinal control on Toyotas with a comma pedal
* Improved auto-brightness using road-facing camera
* Added "Software" settings page with updater controls
* Audi Q2 2018 support thanks to jyoung8607!
* Hyundai Elantra 2021 support thanks to CruiseBrantley!
* Lexus UX Hybrid 2019-2020 support thanks to brianhaugen2!
* Toyota Avalon Hybrid 2019 support thanks to jbates9011!
* SEAT Leon 2017 & 2020 support thanks to jyoung8607!
* Škoda Octavia 2015 & 2019 support thanks to jyoung8607!
Version 0.8.4 (2021-05-17)
========================
* Delay controls start until system is ready
* Fuzzy car identification, enabled with Community Features toggle
* Localizer optimized for increased precision and less CPU usage
* Re-tuned lateral control to be more aggressive when model is confident
* Toyota Mirai 2021 support
* Lexus NX 300 2020 support thanks to goesreallyfast!
* Volkswagen Atlas 2018-19 support thanks to jyoung8607!
Version 0.8.3 (2021-04-01)
========================
* New model
* Trained on new diverse dataset from 2000+ users from 30+ countries
* Trained with improved segnet from the comma-pencil community project
* 🥬 Dramatically improved end-to-end lateral performance 🥬
* Toggle added to disable the use of lanelines
* NEOS update: update packages and support for new UI
* New offroad UI based on Qt
* Default SSH key only used for setup
* Kia Ceed 2019 support thanks to ZanZaD13!
* Kia Seltos 2021 support thanks to speedking456!
* Added support for many Volkswagen and Škoda models thanks to jyoung8607!
Version 0.8.2 (2021-02-26)
========================
* Use model points directly in MPC (no more polyfits), making lateral planning more accurate
* Use model heading prediction for smoother lateral control
* Smarter actuator delay compensation
* Improve qcamera resolution for improved video in explorer and connect
* Adjust maximum engagement speed to better fit the model's training distribution
* New driver monitoring model trained with 3x more diverse data
* Improved face detection with masks
* More predictable DM alerts when visibility is bad
* Rewritten video streaming between openpilot processes
* Improved longitudinal tuning on TSS2 Corolla and Rav4 thanks to briskspirit!
* Audi A3 2015 and 2017 support thanks to keeleysam!
* Nissan Altima 2020 support thanks to avolmensky!
* Lexus ES Hybrid 2018 support thanks to TheInventorMan!
* Toyota Camry Hybrid 2021 support thanks to alancyau!
Version 0.8.1 (2020-12-21)
========================
* Original EON is deprecated, upgrade to comma two
* Better model performance in heavy rain
* Better lane positioning in turns
* Fixed bug where model would cut turns on empty roads at night
* Fixed issue where some Toyotas would not completely stop thanks to briskspirit!
* Toyota Camry 2021 with TSS2.5 support
* Hyundai Ioniq Electric 2020 support thanks to baldwalker!
Version 0.8.0 (2020-11-30)
========================
* New driving model: fully 3D and improved cut-in detection
* UI draws 2 road edges, 4 lanelines and paths in 3D
* Major fixes to cut-in detection for openpilot longitudinal
* Grey panda is no longer supported, upgrade to comma two or black panda
* Lexus NX 2018 support thanks to matt12eagles!
* Kia Niro EV 2020 support thanks to nickn17!
* Toyota Prius 2021 support thanks to rav4kumar!
* Improved lane positioning with uncertain lanelines, wide lanes and exits
* Improved lateral control for Prius and Subaru
Version 0.7.10 (2020-10-29)
========================
* Grey panda is deprecated, upgrade to comma two or black panda
* NEOS update: update to Python 3.8.2 and lower CPU frequency
* Improved thermals due to reduced CPU frequency
* Update SNPE to 1.41.0
* Reduced offroad power consumption
* Various system stability improvements
* Acura RDX 2020 support thanks to csouers!
Version 0.7.9 (2020-10-09)
========================
* Improved car battery power management
* Improved updater robustness
* Improved realtime performance
* Reduced UI and modeld lags
* Increased torque on 2020 Hyundai Sonata and Palisade
Version 0.7.8 (2020-08-19)
========================
* New driver monitoring model: improved face detection and better compatibility with sunglasses
* Download NEOS operating system updates in the background
* Improved updater reliability and responsiveness
* Hyundai Kona 2020, Veloster 2019, and Genesis G70 2018 support thanks to xps-genesis!
Version 0.7.7 (2020-07-20)
========================
* White panda is no longer supported, upgrade to comma two or black panda
* Improved vehicle model estimation using high precision localizer
* Improved thermal management on comma two
* Improved autofocus for road-facing camera
* Improved noise performance for driver-facing camera
* Block lane change start using blindspot monitor on select Toyota, Hyundai, and Subaru
* Fix GM ignition detection
* Code cleanup and smaller release sizes
* Hyundai Sonata 2020 promoted to officially supported car
* Hyundai Ioniq Electric Limited 2019 and Ioniq SE 2020 support thanks to baldwalker!
* Subaru Forester 2019 and Ascent 2019 support thanks to martinl!
Version 0.7.6.1 (2020-06-16)
========================
* Hotfix: update kernel on some comma twos (orders #8570-#8680)
Version 0.7.6 (2020-06-05)
========================
* White panda is deprecated, upgrade to comma two or black panda
* 2017 Nissan X-Trail, 2018-19 Leaf and 2019 Rogue support thanks to avolmensky!
* 2017 Mazda CX-5 support in dashcam mode thanks to Jafaral!
* Huge CPU savings in modeld by using thneed!
* Lots of code cleanup and refactors
Version 0.7.5 (2020-05-13)
========================
* Right-Hand Drive support for both driving and driver monitoring!
* New driving model: improved at sharp turns and lead speed estimation
* New driver monitoring model: overall improvement on comma two
* Driver camera preview in settings to improve mounting position
* Added support for many Hyundai, Kia, Genesis models thanks to xx979xx!
* Improved lateral tuning for 2020 Toyota Rav 4 (hybrid)
Version 0.7.4 (2020-03-20)
========================
* New driving model: improved lane changes and lead car detection
* Improved driver monitoring model: improve eye detection
* Improved calibration stability
* Improved lateral control on some 2019 and 2020 Toyota Prius
* Improved lateral control on VW Golf: 20% more steering torque
* Fixed bug where some 2017 and 2018 Toyota C-HR would use the wrong steering angle sensor
* Support for Honda Insight thanks to theantihero!
* Code cleanup in car abstraction layers and ui
Version 0.7.3 (2020-02-21)
========================
* Support for 2020 Highlander thanks to che220!
* Support for 2018 Lexus NX 300h thanks to kengggg!
* Speed up ECU firmware query
* Fix bug where manager would sometimes hang after shutting down the car
Version 0.7.2 (2020-02-07)
========================
* ECU firmware version based fingerprinting for Honda & Toyota
* New driving model: improved path prediction during turns and lane changes and better lead speed tracking
* Improve driver monitoring under extreme lighting and add low accuracy alert
* Support for 2019 Rav4 Hybrid thanks to illumiN8i!
* Support for 2016, 2017 and 2020 Lexus RX thanks to illumiN8i!
* Support for 2020 Chrysler Pacifica Hybrid thanks to adhintz!
Version 0.7.1 (2020-01-20)
========================
* comma two support!
* Lane Change Assist above 45 mph!
* Replace zmq with custom messaging library, msgq!
* Supercombo model: calibration and driving models are combined for better lead estimate
* More robust updater thanks to jyoung8607! Requires NEOS update
* Improve low speed ACC tuning
Version 0.7 (2019-12-13)
========================
* Move to SCons build system!
* Add Lane Departure Warning (LDW) for all supported vehicles!
* NEOS update: increase wifi speed thanks to jyoung8607!
* Adaptive driver monitoring based on scene
* New driving model trained end-to-end: improve lane lines and lead detection
* Smarter torque limit alerts for all cars
* Improve GM longitudinal control: proper computations for 15Hz radar
* Move GM port, Toyota with DSU removed, comma pedal in community features; toggle switch required
* Remove upload over cellular toggle: only upload qlog and qcamera files if not on wifi
* Refactor Panda code towards ISO26262 and SIL2 compliance
* Forward stock FCW for Honda Nidec
* Volkswagen port now standard: comma Harness intercepts stock camera
Version 0.6.6 (2019-11-05)
========================
* Volkswagen support thanks to jyoung8607!
* Toyota Corolla Hybrid with TSS 2.0 support thanks to u8511049!
* Lexus ES with TSS 2.0 support thanks to energee!
* Fix GM ignition detection and lock safety mode not required anymore
* Log panda firmware and dongle ID thanks to martinl!
* New driving model: improve path prediction and lead detection
* New driver monitoring model, 4x smaller and running on DSP
* Display an alert and don't start openpilot if panda has wrong firmware
* Fix bug preventing EON from terminating processes after a drive
* Remove support for Toyota giraffe without the 120Ohm resistor
Version 0.6.5 (2019-10-07)
========================
* NEOS update: upgrade to Python3 and new installer!
* comma Harness support!
* New driving model: improve path prediction
* New driver monitoring model: more accurate face and eye detection
* Redesign offroad screen to display updates and alerts
* Increase maximum allowed acceleration
* Prevent car 12V battery drain by cutting off EON charge after 3 days of no drive
* Lexus CT Hybrid support thanks to thomaspich!
* Louder chime for critical alerts
* Add toggle to switch to dashcam mode
* Fix "invalid vehicle params" error on DSU-less Toyota
Version 0.6.4 (2019-09-08)
========================
* Forward stock AEB for Honda Nidec
* Improve lane centering on banked roads
* Always-on forward collision warning
* Always-on driver monitoring, except for right hand drive countries
* Driver monitoring learns the user's normal driving position
* Honda Fit support thanks to energee!
* Lexus IS support
Version 0.6.3 (2019-08-12)
========================
* Alert sounds from EON: requires NEOS update
@@ -38,7 +629,7 @@ Version 0.6 (2019-07-01)
* Panda safety code is MISRA compliant and ships with a signed version on release2
* New NEOS is 500MB smaller and has a reproducible usr/pipenv
* Lexus ES Hybrid support thanks to wocsor!
* Improve tuning for supported Toyota with TSS2
* Improve tuning for supported Toyota with TSS 2.0
* Various other stability improvements
Version 0.5.13 (2019-05-31)
@@ -48,7 +639,7 @@ Version 0.5.13 (2019-05-31)
* Reduce CPU utilization by 20% and improve stability
* Temporarily remove mapd functionalities to improve stability
* Add openpilot record-only mode for unsupported cars
* Synchronize controlsd to boardd to reduce latency
* Synchronize controlsd to pandad to reduce latency
* Remove panda support for Subaru giraffe
Version 0.5.12 (2019-05-16)
@@ -344,96 +935,96 @@ Version 0.3.4 (2017-07-28)
Version 0.3.3 (2017-06-28)
===========================
* Improved model trained on more data
* Alpha CR-V support thanks to energee and johnnwvs!
* Using the opendbc project for DBC files
* Minor performance improvements
* UI update thanks to pjlao307
* Power off button
* 6% more torque on the Civic
* Improved model trained on more data
* Alpha CR-V support thanks to energee and johnnwvs!
* Using the opendbc project for DBC files
* Minor performance improvements
* UI update thanks to pjlao307
* Power off button
* 6% more torque on the Civic
Version 0.3.2 (2017-05-22)
===========================
* Minor stability bugfixes
* Added metrics and rear view mirror disable to settings
* Update model with more crowdsourced data
* Minor stability bugfixes
* Added metrics and rear view mirror disable to settings
* Update model with more crowdsourced data
Version 0.3.1 (2017-05-17)
===========================
* visiond stability bugfix
* Add logging for angle and flashing
* visiond stability bugfix
* Add logging for angle and flashing
Version 0.3.0 (2017-05-12)
===========================
* Add CarParams struct to improve the abstraction layer
* Refactor visiond IPC to support multiple clients
* Add raw GPS and beginning support for navigation
* Improve model in visiond using crowdsourced data
* Add improved system logging to diagnose instability
* Rewrite baseui in React Native
* Moved calibration to the cloud
* Add CarParams struct to improve the abstraction layer
* Refactor visiond IPC to support multiple clients
* Add raw GPS and beginning support for navigation
* Improve model in visiond using crowdsourced data
* Add improved system logging to diagnose instability
* Rewrite baseui in React Native
* Moved calibration to the cloud
Version 0.2.9 (2017-03-01)
===========================
* Retain compatibility with NEOS v1
* Retain compatibility with NEOS v1
Version 0.2.8 (2017-02-27)
===========================
* Fix bug where frames were being dropped in minute 71
* Fix bug where frames were being dropped in minute 71
Version 0.2.7 (2017-02-08)
===========================
* Better performance and pictures at night
* Fix ptr alignment issue in boardd
* Fix brake error light, fix crash if too cold
* Better performance and pictures at night
* Fix ptr alignment issue in pandad
* Fix brake error light, fix crash if too cold
Version 0.2.6 (2017-01-31)
===========================
* Fix bug in visiond model execution
* Fix bug in visiond model execution
Version 0.2.5 (2017-01-30)
===========================
* Fix race condition in manager
* Fix race condition in manager
Version 0.2.4 (2017-01-27)
===========================
* OnePlus 3T support
* Enable installation as NEOS app
* Various minor bugfixes
* OnePlus 3T support
* Enable installation as NEOS app
* Various minor bugfixes
Version 0.2.3 (2017-01-11)
===========================
* Reduce space usage by 80%
* Add better logging
* Add Travis CI
* Reduce space usage by 80%
* Add better logging
* Add Travis CI
Version 0.2.2 (2017-01-10)
===========================
* Board triggers started signal on CAN messages
* Improved autoexposure
* Handle out of space, improve upload status
* Board triggers started signal on CAN messages
* Improved autoexposure
* Handle out of space, improve upload status
Version 0.2.1 (2016-12-14)
===========================
* Performance improvements, removal of more numpy
* Fix boardd process priority
* Make counter timer reset on use of steering wheel
* Performance improvements, removal of more numpy
* Fix pandad process priority
* Make counter timer reset on use of steering wheel
Version 0.2 (2016-12-12)
=========================
* Car/Radar abstraction layers have shipped, see cereal/car.capnp
* controlsd has been refactored
* Shipped plant model and testing maneuvers
* visiond exits more gracefully now
* Hardware encoder in visiond should always init
* ui now turns off the screen after 30 seconds
* Switch to openpilot release branch for future releases
* Added preliminary Docker container to run tests on PC
* Car/Radar abstraction layers have shipped, see cereal/car.capnp
* controlsd has been refactored
* Shipped plant model and testing maneuvers
* visiond exits more gracefully now
* Hardware encoder in visiond should always init
* ui now turns off the screen after 30 seconds
* Switch to openpilot release branch for future releases
* Added preliminary Docker container to run tests on PC
Version 0.1 (2016-11-29)
=========================
* Initial release of openpilot
* Adaptive cruise control is working
* Lane keep assist is working
* Support for Acura ILX 2016 with AcuraWatch Plus
* Support for Honda Civic 2016 Touring Edition
* Initial release of openpilot
* Adaptive cruise control is working
* Lane keep assist is working
* Support for Acura ILX 2016 with AcuraWatch Plus
* Support for Honda Civic 2016 Touring Edition
-151
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@@ -1,151 +0,0 @@
openpilot Safety
======
openpilot is an Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA) system.
Like other ACC and LKA systems, openpilot requires the driver to be alert and to
pay attention at all times. We repeat, **driver alertness is necessary, but not
sufficient, for openpilot to be used safely**.
In order to enforce driver alertness, openpilot includes a driver monitoring feature
that alerts the driver when distracted.
However, even with an attentive driver, we must make further efforts for the system to be
safe. We have designed openpilot with two other safety considerations.
1. The driver must always be capable to immediately retake manual control of the vehicle,
by stepping on either pedal or by pressing the cancel button.
2. The vehicle must not alter its trajectory too quickly for the driver to safely
react. This means that while the system is engaged, the actuators are constrained
to operate within reasonable limits.
Following are details of the car specific safety implementations:
Honda/Acura
------
- While the system is engaged, gas, brake and steer commands are subject to the same limits used by
the stock system.
- Without an interceptor, the gas is controlled by the Powertrain Control Module (PCM).
The PCM limits acceleration to what is reasonable for a cruise control system. With an
interceptor, the gas is clipped to 60%.
- The brake is controlled by the 0x1FA CAN message. This message allows full
braking, although the panda firmware and openpilot clip it to 1/4th of the max.
This is approximately 0.3g of braking.
- Steering is controlled by the 0xE4 CAN message. The Electronic Power Steering (EPS)
controller in the car limits the torque to a very small amount, so regardless of the
message, the controller cannot jerk the wheel.
- Brake and gas pedal pressed signals are contained in the 0x17C CAN message. A rising edge of
either signals triggers a disengagement, which is enforced by the panda firmware and by openpilot. The
white led on the panda signifies if the panda is allowing control messages.
- Honda CAN uses both a counter and a checksum to ensure integrity and prevent
replay of the same message.
Toyota/Lexus
------
- While the system is engaged, gas, brake and steer commands are subject to the same limits used by
the stock system.
- With the stock Driving Support Unit (DSU) connected (or in DSU-less models like Camry and C-HR),
the acceleration is controlled by the stock system and is subject to the stock adaptive cruise
control limits. Without the stock DSU connected, the acceleration command is controlled by the
0x343 CAN message and its value is limited between .3g of deceleration and .15g of acceleration
by the panda firmware and by openpilot. The acceleration command is ignored by the Engine Control
Module (ECM) while the cruise control system is disengaged.
- Steering torque is controlled through the 0x2E4 CAN message and it's limited by the panda firmware and by
openpilot to a value between -1500 and 1500. In addition, the vehicle EPS unit will not respond to
commands outside these limits. A steering torque rate limit is enforced by the panda firmware and by
openpilot, so that the commanded steering torque must rise from 0 to max value no faster than
1.5s. Commanded steering torque is limited by the panda firmware and by openpilot to be no more than 350
units above the actual EPS generated motor torque to ensure limited differences between
commanded and actual torques.
- Brake and gas pedal pressed signals are contained in the 0x224 and 0x1D2 CAN messages,
respectively. A rising edge of either signals triggers a disengagement, which is enforced by the
panda firmware and by openpilot. Additionally, the cruise control system disengages on the rising edge of
the brake pedal pressed signal.
- The cruise control system state is contained in the 0x1D2 message. No control messages are
allowed if the cruise control system is not active. This is enforced by openpilot and the
panda firmware. The white led on the panda signifies if the panda is allowing control messages.
GM/Chevrolet
------
- While the system is engaged, gas, brake and steer commands are subject to the same limits used by
the stock system.
- The gas and regen are controlled by the 0x2CB message and it's limited by the panda firmware and by
openpilot to a value between 1404 and 3072. the minimum value correspond to a mild decel due to regen,
while 3072 correspond to approximately 0.18g of acceleration from stop.
- The friction brakes are controlled by the 0x315 message and its value is limited by the panda firmware
and openpilot to 350. This is approximately 0.3g of braking.
- Steering torque is controlled through the 0x180 CAN message and it's limited by the panda firmware and by
openpilot to a value between -300 and 300. In addition, the vehicle EPS unit will fault for
commands outside these limits. A steering torque rate limit is enforced by the panda firmware and by
openpilot, so that the commanded steering torque must rise from 0 to max value no faster than
0.75s. Commanded steering torque is gradually limited by the panda firmware and by openpilot if the driver's
torque exceeds 12 units in the opposite dicrection to ensure limited applied torque against the
driver's will.
- Brake pedal and gas pedal potentiometer signals are contained in the 0xF1 and 0x1A1 CAN messages,
respectively. A rising edge of either signals triggers a disengagement, which is enforced by the
panda firmware and by openpilot. Additionally, the cruise control system disengages on the rising edge of
the brake pedal pressed signal. The regen paddle pressed signal is in the 0xBD message. When the
regen paddle is pressed, a disengagement is enforced by both the firmware and by openpilot.
- GM CAN uses both a counter and a checksum to ensure integrity and prevent
replay of the same message.
Hyundai/Kia (Lateral only)
------
- While the system is engaged, steer commands are subject to the same limits used by
the stock system.
- Steering torque is controlled through the 0x340 CAN message and it's limited by the panda firmware and by
openpilot to a value between -255 and 255. In addition, the vehicle EPS unit will fault for
commands outside the values of -409 and 409. A steering torque rate limit is enforced by the panda firmware and by
openpilot, so that the commanded steering torque must rise from 0 to max value no faster than
0.85s. Commanded steering torque is gradually limited by the panda firmware and by openpilot if the driver's
torque exceeds 50 units in the opposite dicrection to ensure limited applied torque against the
driver's will.
Chrysler/Jeep/Fiat (Lateral only)
------
- While the system is engaged, steer commands are subject to the same limits used by
the stock system.
- Steering torque is controlled through the 0x292 CAN message and it's limited by the panda firmware and by
openpilot to a value between -261 and 261. In addition, the vehicle EPS unit will fault for
commands outside these limits. A steering torque rate limit is enforced by the panda firmware and by
openpilot, so that the commanded steering torque must rise from 0 to max value no faster than
0.87s. Commanded steering torque is limited by the panda firmware and by openpilot to be no more than 80
units above the actual EPS generated motor torque to ensure limited differences between
commanded and actual torques.
Subaru (Lateral only)
------
- While the system is engaged, steer commands are subject to the same limits used by
the stock system.
- Steering torque is controlled through the 0x122 CAN message and it's limited by the panda firmware and by
openpilot to a value between -255 and 255. In addition, the vehicle EPS unit will fault for
commands outside the values of -2047 and 2047. A steering torque rate limit is enforced by the panda firmware and by
openpilot, so that the commanded steering torque must rise from 0 to max value no faster than
0.41s. Commanded steering torque is gradually limited by the panda firmware and by openpilot if the driver's
torque exceeds 60 units in the opposite dicrection to ensure limited applied torque against the
driver's will.
**Extra note**: comma.ai strongly discourages the use of openpilot forks with safety code either missing or
not fully meeting the above requirements.
+5
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@@ -0,0 +1,5 @@
# Security Policy
## Reporting a Vulnerability
Suspected vulnerabilities can be reported to both `adeeb@comma.ai` and `security@comma.ai`.
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-6
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@@ -1,6 +0,0 @@
gen
node_modules
package-lock.json
*.pyc
__pycache__
-62
View File
@@ -1,62 +0,0 @@
PWD := $(shell pwd)
SRCS := log.capnp car.capnp
GENS := gen/cpp/car.capnp.c++ gen/cpp/log.capnp.c++
JS := gen/js/car.capnp.js gen/js/log.capnp.js
UNAME_M ?= $(shell uname -m)
GENS += gen/c/car.capnp.c gen/c/log.capnp.c gen/c/include/c++.capnp.h gen/c/include/java.capnp.h
ifeq ($(UNAME_M),x86_64)
ifneq (, $(shell which capnpc-java))
GENS += gen/java/Car.java gen/java/Log.java
else
$(warning capnpc-java not found, skipping java build)
endif
endif
ifeq ($(UNAME_M),aarch64)
CAPNPC=PATH=$(PWD)/../phonelibs/capnp-cpp/aarch64/bin/:$$PATH capnpc
else
CAPNPC=capnpc
endif
.PHONY: all
all: $(GENS)
js: $(JS)
.PHONY: clean
clean:
rm -rf gen
rm -rf node_modules
rm -rf package-lock.json
gen/c/%.capnp.c: %.capnp
@echo "[ CAPNPC C ] $@"
mkdir -p gen/c/
$(CAPNPC) '$<' -o c:gen/c/
gen/js/%.capnp.js: %.capnp
@echo "[ CAPNPC JavaScript ] $@"
mkdir -p gen/js/
sh ./generate_javascript.sh
gen/cpp/%.capnp.c++: %.capnp
@echo "[ CAPNPC C++ ] $@"
mkdir -p gen/cpp/
$(CAPNPC) '$<' -o c++:gen/cpp/
gen/java/Car.java gen/java/Log.java: $(SRCS)
@echo "[ CAPNPC java ] $@"
mkdir -p gen/java/
$(CAPNPC) $^ -o java:gen/java
# c-capnproto needs some empty headers
gen/c/include/c++.capnp.h gen/c/include/java.capnp.h:
mkdir -p gen/c/include
touch '$@'
+60
View File
@@ -0,0 +1,60 @@
# What is cereal? [![cereal tests](https://github.com/commaai/cereal/workflows/tests/badge.svg?event=push)](https://github.com/commaai/cereal/actions) [![codecov](https://codecov.io/gh/commaai/cereal/branch/master/graph/badge.svg)](https://codecov.io/gh/commaai/cereal)
cereal is both a messaging spec for robotics systems as well as generic high performance IPC pub sub messaging with a single publisher and multiple subscribers.
Imagine this use case:
* A sensor process reads gyro measurements directly from an IMU and publishes a `sensorEvents` packet
* A calibration process subscribes to the `sensorEvents` packet to use the IMU
* A localization process subscribes to the `sensorEvents` packet to use the IMU also
## Messaging Spec
You'll find the message types in [log.capnp](log.capnp). It uses [Cap'n proto](https://capnproto.org/capnp-tool.html) and defines one struct called `Event`.
All `Events` have a `logMonoTime` and a `valid`. Then a big union defines the packet type.
### Best Practices
- **All fields must describe quantities in SI units**, unless otherwise specified in the field name.
- In the context of the message they are in, field names should be completely unambiguous.
- All values should be easy to plot and be human-readable with minimal parsing.
### Maintaining backwards-compatibility
When making changes to the messaging spec you want to maintain backwards-compatibility, such that old logs can
be parsed with a new version of cereal. Adding structs and adding members to structs is generally safe, most other
things are not. Read more details [here](https://capnproto.org/language.html).
### Custom forks
Forks of [openpilot](https://github.com/commaai/openpilot) might want to add things to the messaging
spec, however this could conflict with future changes made in mainline cereal/openpilot. Rebasing against mainline openpilot
then means breaking backwards-compatibility with all old logs of your fork. So we added reserved events in
[custom.capnp](custom.capnp) that we will leave empty in mainline cereal/openpilot. **If you only modify those, you can ensure your
fork will remain backwards-compatible with all versions of mainline cereal/openpilot and your fork.**
## Pub Sub Backends
cereal supports two backends, one based on [zmq](https://zeromq.org/) and another called [msgq](messaging/msgq.cc), a custom pub sub based on shared memory that doesn't require the bytes to pass through the kernel.
Example
---
```python
import cereal.messaging as messaging
# in subscriber
sm = messaging.SubMaster(['sensorEvents'])
while 1:
sm.update()
print(sm['sensorEvents'])
```
```python
# in publisher
pm = messaging.PubMaster(['sensorEvents'])
dat = messaging.new_message('sensorEvents', size=1)
dat.sensorEvents[0] = {"gyro": {"v": [0.1, -0.1, 0.1]}}
pm.send('sensorEvents', dat)
```
+1
View File
@@ -6,3 +6,4 @@ capnp.remove_import_hook()
log = capnp.load(os.path.join(CEREAL_PATH, "log.capnp"))
car = capnp.load(os.path.join(CEREAL_PATH, "car.capnp"))
custom = capnp.load(os.path.join(CEREAL_PATH, "custom.capnp"))
+447 -97
View File
@@ -1,31 +1,28 @@
using Cxx = import "./include/c++.capnp";
$Cxx.namespace("cereal");
using Java = import "./include/java.capnp";
$Java.package("ai.comma.openpilot.cereal");
$Java.outerClassname("Car");
@0x8e2af1e708af8b8d;
# ******* events causing controls state machine transition *******
struct CarEvent @0x9b1657f34caf3ad3 {
name @0 :EventName;
# event types
enable @1 :Bool;
noEntry @2 :Bool;
warning @3 :Bool;
warning @3 :Bool; # alerts presented only when enabled or soft disabling
userDisable @4 :Bool;
softDisable @5 :Bool;
immediateDisable @6 :Bool;
preEnable @7 :Bool;
permanent @8 :Bool;
permanent @8 :Bool; # alerts presented regardless of openpilot state
overrideLateral @10 :Bool;
overrideLongitudinal @9 :Bool;
enum EventName @0xbaa8c5d505f727de {
# TODO: copy from error list
canError @0;
steerUnavailable @1;
brakeUnavailable @2;
gasUnavailable @3;
wrongGear @4;
doorOpen @5;
seatbeltNotLatched @6;
@@ -35,50 +32,149 @@ struct CarEvent @0x9b1657f34caf3ad3 {
reverseGear @10;
buttonCancel @11;
buttonEnable @12;
pedalPressed @13;
pedalPressed @13; # exits active state
preEnableStandstill @73; # added during pre-enable state with brake
gasPressedOverride @108; # added when user is pressing gas with no disengage on gas
steerOverride @114;
cruiseDisabled @14;
radarCanError @15;
dataNeeded @16;
speedTooLow @17;
outOfSpace @18;
overheat @19;
calibrationIncomplete @20;
calibrationInvalid @21;
calibrationRecalibrating @117;
controlsMismatch @22;
pcmEnable @23;
pcmDisable @24;
noTarget @25;
radarFault @26;
modelCommIssueDEPRECATED @27;
brakeHold @28;
parkBrake @29;
manualRestart @30;
lowSpeedLockout @31;
plannerError @32;
ipasOverride @33;
debugAlert @34;
steerTempUnavailableMute @35;
joystickDebug @34;
steerTempUnavailableSilent @35;
resumeRequired @36;
preDriverDistracted @37;
promptDriverDistracted @38;
driverDistracted @39;
geofence @40;
driverMonitorOn @41;
driverMonitorOff @42;
preDriverUnresponsive @43;
promptDriverUnresponsive @44;
driverUnresponsive @45;
belowSteerSpeed @46;
calibrationProgress @47;
lowBattery @48;
invalidGiraffeHonda @49;
vehicleModelInvalid @50;
controlsFailed @51;
accFaulted @51;
sensorDataInvalid @52;
commIssue @53;
commIssueAvgFreq @109;
tooDistracted @54;
posenetInvalid @55;
soundsUnavailable @56;
preLaneChangeLeft @57;
preLaneChangeRight @58;
laneChange @59;
lowMemory @63;
stockAeb @64;
ldw @65;
carUnrecognized @66;
invalidLkasSetting @69;
speedTooHigh @70;
laneChangeBlocked @71;
relayMalfunction @72;
stockFcw @74;
startup @75;
startupNoCar @76;
startupNoControl @77;
startupNoSecOcKey @121;
startupMaster @78;
startupNoFw @104;
fcw @79;
steerSaturated @80;
belowEngageSpeed @84;
noGps @85;
wrongCruiseMode @87;
modeldLagging @89;
deviceFalling @90;
fanMalfunction @91;
cameraMalfunction @92;
cameraFrameRate @110;
processNotRunning @95;
dashcamMode @96;
controlsInitializing @98;
usbError @99;
roadCameraError @100;
driverCameraError @101;
wideRoadCameraError @102;
highCpuUsage @105;
cruiseMismatch @106;
lkasDisabled @107;
canBusMissing @111;
controlsdLagging @112;
resumeBlocked @113;
steerTimeLimit @115;
vehicleSensorsInvalid @116;
locationdTemporaryError @103;
locationdPermanentError @118;
paramsdTemporaryError @50;
paramsdPermanentError @119;
actuatorsApiUnavailable @120;
# FrogPilot Events
accel30 @122;
accel35 @123;
accel40 @124;
blockUser @125;
customStartupAlert @126;
dejaVuCurve @127;
firefoxSteerSaturated @128;
forcingStop @129;
goatSteerSaturated @130;
greenLight @131;
hal9000 @132;
holidayActive @133;
laneChangeBlockedLoud @134;
leadDeparting @135;
noLaneAvailable @136;
openpilotCrashed @137;
openpilotCrashedRandomEvent @138;
pedalInterceptorNoBrake @139;
speedLimitChanged @140;
thisIsFineSteerSaturated @141;
toBeContinued @142;
torqueNNLoad @143;
trafficModeActive @144;
trafficModeInactive @145;
turningLeft @146;
turningRight @147;
vCruise69 @148;
yourFrogTriedToKillMe @149;
youveGotMail @150;
radarCanErrorDEPRECATED @15;
communityFeatureDisallowedDEPRECATED @62;
radarCommIssueDEPRECATED @67;
driverMonitorLowAccDEPRECATED @68;
gasUnavailableDEPRECATED @3;
dataNeededDEPRECATED @16;
modelCommIssueDEPRECATED @27;
ipasOverrideDEPRECATED @33;
geofenceDEPRECATED @40;
driverMonitorOnDEPRECATED @41;
driverMonitorOffDEPRECATED @42;
calibrationProgressDEPRECATED @47;
invalidGiraffeHondaDEPRECATED @49;
invalidGiraffeToyotaDEPRECATED @60;
internetConnectivityNeededDEPRECATED @61;
whitePandaUnsupportedDEPRECATED @81;
commIssueWarningDEPRECATED @83;
focusRecoverActiveDEPRECATED @86;
neosUpdateRequiredDEPRECATED @88;
modelLagWarningDEPRECATED @93;
startupOneplusDEPRECATED @82;
startupFuzzyFingerprintDEPRECATED @97;
noTargetDEPRECATED @25;
brakeUnavailableDEPRECATED @2;
plannerErrorDEPRECATED @32;
gpsMalfunctionDEPRECATED @94;
}
}
@@ -86,32 +182,50 @@ struct CarEvent @0x9b1657f34caf3ad3 {
# all speeds in m/s
struct CarState {
errorsDEPRECATED @0 :List(CarEvent.EventName);
events @13 :List(CarEvent);
# CAN health
canValid @26 :Bool; # invalid counter/checksums
canTimeout @40 :Bool; # CAN bus dropped out
canErrorCounter @48 :UInt32;
# car speed
vEgo @1 :Float32; # best estimate of speed
aEgo @16 :Float32; # best estimate of acceleration
vEgoRaw @17 :Float32; # unfiltered speed from CAN sensors
vEgo @1 :Float32; # best estimate of speed
aEgo @16 :Float32; # best estimate of acceleration
vEgoRaw @17 :Float32; # unfiltered speed from CAN sensors
vEgoCluster @44 :Float32; # best estimate of speed shown on car's instrument cluster, used for UI
yawRate @22 :Float32; # best estimate of yaw rate
standstill @18 :Bool;
wheelSpeeds @2 :WheelSpeeds;
# gas pedal, 0.0-1.0
gas @3 :Float32; # this is user + computer
gas @3 :Float32; # this is user pedal only
gasPressed @4 :Bool; # this is user pedal only
engineRpm @46 :Float32;
# brake pedal, 0.0-1.0
brake @5 :Float32; # this is user pedal only
brakePressed @6 :Bool; # this is user pedal only
brakeLights @19 :Bool;
regenBraking @45 :Bool; # this is user pedal only
parkingBrake @39 :Bool;
brakeHoldActive @38 :Bool;
# steering wheel
steeringAngle @7 :Float32; # deg
steeringRate @15 :Float32; # deg/s
steeringTorque @8 :Float32; # TODO: standardize units
steeringAngleDeg @7 :Float32;
steeringAngleOffsetDeg @37 :Float32; # Offset betweens sensors in case there multiple
steeringRateDeg @15 :Float32;
steeringTorque @8 :Float32; # TODO: standardize units
steeringTorqueEps @27 :Float32; # TODO: standardize units
steeringPressed @9 :Bool; # if the user is using the steering wheel
steeringPressed @9 :Bool; # if the user is using the steering wheel
steerFaultTemporary @35 :Bool; # temporary EPS fault
steerFaultPermanent @36 :Bool; # permanent EPS fault
stockAeb @30 :Bool;
stockFcw @31 :Bool;
espDisabled @32 :Bool;
accFaulted @42 :Bool;
carFaultedNonCritical @47 :Bool; # some ECU is faulted, but car remains controllable
# cruise state
cruiseState @10 :CruiseState;
@@ -128,10 +242,19 @@ struct CarState {
# lock info
doorOpen @24 :Bool;
seatbeltUnlatched @25 :Bool;
canValid @26 :Bool;
# which packets this state came from
canMonoTimes @12: List(UInt64);
# clutch (manual transmission only)
clutchPressed @28 :Bool;
# blindspot sensors
leftBlindspot @33 :Bool; # Is there something blocking the left lane change
rightBlindspot @34 :Bool; # Is there something blocking the right lane change
fuelGauge @41 :Float32; # battery or fuel tank level from 0.0 to 1.0
charging @43 :Bool;
# process meta
cumLagMs @50 :Float32;
struct WheelSpeeds {
# optional wheel speeds
@@ -144,9 +267,11 @@ struct CarState {
struct CruiseState {
enabled @0 :Bool;
speed @1 :Float32;
speedCluster @6 :Float32; # Set speed as shown on instrument cluster
available @2 :Bool;
speedOffset @3 :Float32;
standstill @4 :Bool;
nonAdaptive @5 :Bool;
}
enum GearShifter {
@@ -158,9 +283,10 @@ struct CarState {
sport @5;
low @6;
brake @7;
eco @8;
manumatic @9;
}
# send on change
struct ButtonEvent {
pressed @0 :Bool;
@@ -176,8 +302,18 @@ struct CarState {
altButton1 @6;
altButton2 @7;
altButton3 @8;
setCruise @9;
resumeCruise @10;
gapAdjustCruise @11;
}
}
# deprecated
errorsDEPRECATED @0 :List(CarEvent.EventName);
brakeLightsDEPRECATED @19 :Bool;
steeringRateLimitedDEPRECATED @29 :Bool;
canMonoTimesDEPRECATED @12: List(UInt64);
canRcvTimeoutDEPRECATED @49 :Bool;
}
# ******* radar state @ 20hz *******
@@ -186,9 +322,6 @@ struct RadarData @0x888ad6581cf0aacb {
errors @0 :List(Error);
points @1 :List(RadarPoint);
# which packets this state came from
canMonoTimes @2 :List(UInt64);
enum Error {
canError @0;
fault @1;
@@ -212,6 +345,9 @@ struct RadarData @0x888ad6581cf0aacb {
# some radars flag measurements VS estimates
measured @6 :Bool;
}
# deprecated
canMonoTimesDEPRECATED @2 :List(UInt64);
}
# ******* car controls @ 100hz *******
@@ -219,14 +355,21 @@ struct RadarData @0x888ad6581cf0aacb {
struct CarControl {
# must be true for any actuator commands to work
enabled @0 :Bool;
active @7 :Bool;
gasDEPRECATED @1 :Float32;
brakeDEPRECATED @2 :Float32;
steeringTorqueDEPRECATED @3 :Float32;
latActive @11: Bool;
longActive @12: Bool;
# Actuator commands as computed by controlsd
actuators @6 :Actuators;
# moved to CarOutput
actuatorsOutputDEPRECATED @10 :Actuators;
leftBlinker @15: Bool;
rightBlinker @16: Bool;
orientationNED @13 :List(Float32);
angularVelocity @14 :List(Float32);
cruiseControl @4 :CruiseControl;
hudControl @5 :HUDControl;
@@ -236,14 +379,30 @@ struct CarControl {
brake @1: Float32;
# range from -1.0 - 1.0
steer @2: Float32;
steerAngle @3: Float32;
# value sent over can to the car
steerOutputCan @8: Float32;
steeringAngleDeg @3: Float32;
curvature @7: Float32;
speed @6: Float32; # m/s
accel @4: Float32; # m/s^2
longControlState @5: LongControlState;
enum LongControlState @0xe40f3a917d908282{
off @0;
pid @1;
stopping @2;
starting @3;
}
}
struct CruiseControl {
cancel @0: Bool;
override @1: Bool;
speedOverride @2: Float32;
accelOverride @3: Float32;
resume @1: Bool;
override @4: Bool;
speedOverrideDEPRECATED @2: Float32;
accelOverrideDEPRECATED @3: Float32;
}
struct HUDControl {
@@ -257,6 +416,7 @@ struct CarControl {
leftLaneVisible @7: Bool;
rightLaneDepart @8: Bool;
leftLaneDepart @9: Bool;
leadDistanceBars @10: Int8; # 1-3: 1 is closest, 3 is farthest. some ports may utilize 2-4 bars instead
enum VisualAlert {
# these are the choices from the Honda
@@ -268,21 +428,54 @@ struct CarControl {
wrongGear @4;
seatbeltUnbuckled @5;
speedTooHigh @6;
ldw @7;
}
enum AudibleAlert {
# these are the choices from the Honda
# map as good as you can for your car
none @0;
chimeEngage @1;
chimeDisengage @2;
chimeError @3;
chimeWarning1 @4;
chimeWarning2 @5;
chimeWarningRepeat @6;
chimePrompt @7;
engage @1;
disengage @2;
refuse @3;
warningSoft @4;
warningImmediate @5;
prompt @6;
promptRepeat @7;
promptDistracted @8;
# FrogPilot sounds
angry @9;
continued @10;
dejaVu @11;
doc @12;
fart @13;
firefox @14;
goat @15;
hal9000 @16;
mail @17;
nessie @18;
noice @19;
startup @20;
thisIsFine @21;
uwu @22;
}
}
gasDEPRECATED @1 :Float32;
brakeDEPRECATED @2 :Float32;
steeringTorqueDEPRECATED @3 :Float32;
activeDEPRECATED @7 :Bool;
rollDEPRECATED @8 :Float32;
pitchDEPRECATED @9 :Float32;
}
struct CarOutput {
# Any car specific rate limits or quirks applied by
# the CarController are reflected in actuatorsOutput
# and matches what is sent to the car
actuatorsOutput @0 :CarControl.Actuators;
}
# ****** car param ******
@@ -290,59 +483,89 @@ struct CarControl {
struct CarParams {
carName @0 :Text;
carFingerprint @1 :Text;
fuzzyFingerprint @55 :Bool;
enableGasInterceptor @2 :Bool;
enableCruise @3 :Bool;
enableCamera @4 :Bool;
enableDsu @5 :Bool; # driving support unit
enableApgs @6 :Bool; # advanced parking guidance system
notCar @66 :Bool; # flag for non-car robotics platforms
pcmCruise @3 :Bool; # is openpilot's state tied to the PCM's cruise state?
enableDsu @5 :Bool; # driving support unit
enableBsm @56 :Bool; # blind spot monitoring
flags @64 :UInt32; # flags for car specific quirks
experimentalLongitudinalAvailable @71 :Bool;
minEnableSpeed @7 :Float32;
minSteerSpeed @8 :Float32;
safetyModel @9 :SafetyModel;
safetyParam @10 :Int16;
steerMaxBP @11 :List(Float32);
steerMaxV @12 :List(Float32);
gasMaxBP @13 :List(Float32);
gasMaxV @14 :List(Float32);
brakeMaxBP @15 :List(Float32);
brakeMaxV @16 :List(Float32);
safetyConfigs @62 :List(SafetyConfig);
alternativeExperience @65 :Int16; # panda flag for features like no disengage on gas
# Car docs fields
maxLateralAccel @68 :Float32;
autoResumeSng @69 :Bool; # describes whether car can resume from a stop automatically
# things about the car in the manual
mass @17 :Float32; # [kg] running weight
wheelbase @18 :Float32; # [m] distance from rear to front axle
centerToFront @19 :Float32; # [m] GC distance to front axle
steerRatio @20 :Float32; # [] ratio between front wheels and steering wheel angles
steerRatioRear @21 :Float32; # [] rear steering ratio wrt front steering (usually 0)
mass @17 :Float32; # [kg] curb weight: all fluids no cargo
wheelbase @18 :Float32; # [m] distance from rear axle to front axle
centerToFront @19 :Float32; # [m] distance from center of mass to front axle
steerRatio @20 :Float32; # [] ratio of steering wheel angle to front wheel angle
steerRatioRear @21 :Float32; # [] ratio of steering wheel angle to rear wheel angle (usually 0)
# things we can derive
rotationalInertia @22 :Float32; # [kg*m2] body rotational inertia
tireStiffnessFactor @72 :Float32; # scaling factor used in calculating tireStiffness[Front,Rear]
tireStiffnessFront @23 :Float32; # [N/rad] front tire coeff of stiff
tireStiffnessRear @24 :Float32; # [N/rad] rear tire coeff of stiff
longitudinalTuning @25 :LongitudinalPIDTuning;
lateralParams @48 :LateralParams;
lateralTuning :union {
pid @26 :LateralPIDTuning;
indi @27 :LateralINDITuning;
lqr @40 :LateralLQRTuning;
indiDEPRECATED @27 :LateralINDITuning;
lqrDEPRECATED @40 :LateralLQRTuning;
torque @67 :LateralTorqueTuning;
}
steerLimitAlert @28 :Bool;
steerLimitTimer @47 :Float32; # time before steerLimitAlert is issued
vEgoStopping @29 :Float32; # Speed at which the car goes into stopping state
directAccelControl @30 :Bool; # Does the car have direct accel control or just gas/brake
stoppingControl @31 :Bool; # Does the car allows full control even at lows speeds when stopping
startAccel @32 :Float32; # Required acceleraton to overcome creep braking
steerRateCost @33 :Float32; # Lateral MPC cost on steering rate
vEgoStarting @59 :Float32; # Speed at which the car goes into starting state
stoppingControl @31 :Bool; # Does the car allow full control even at lows speeds when stopping
steerControlType @34 :SteerControlType;
radarOffCan @35 :Bool; # True when radar objects aren't visible on CAN
radarUnavailable @35 :Bool; # True when radar objects aren't visible on CAN or aren't parsed out
stopAccel @60 :Float32; # Required acceleration to keep vehicle stationary
stoppingDecelRate @52 :Float32; # m/s^2/s while trying to stop
startAccel @32 :Float32; # Required acceleration to get car moving
startingState @70 :Bool; # Does this car make use of special starting state
steerActuatorDelay @36 :Float32; # Steering wheel actuator delay in seconds
longitudinalActuatorDelay @58 :Float32; # Gas/Brake actuator delay in seconds
openpilotLongitudinalControl @37 :Bool; # is openpilot doing the longitudinal control?
carVin @38 :Text; # VIN number queried during fingerprinting
isPandaBlack @39: Bool;
dashcamOnly @41: Bool;
passive @73: Bool; # is openpilot in control?
transmissionType @43 :TransmissionType;
carFw @44 :List(CarFw);
radarTimeStep @45: Float32 = 0.05; # time delta between radar updates, 20Hz is very standard
fingerprintSource @49: FingerprintSource;
networkLocation @50 :NetworkLocation; # Where Panda/C2 is integrated into the car's CAN network
wheelSpeedFactor @63 :Float32; # Multiplier on wheels speeds to computer actual speeds
secOcRequired @74 :Bool; # Car requires SecOC message authentication to operate
secOcKeyAvailable @75 :Bool; # Stored SecOC key loaded from params
struct SafetyConfig {
safetyModel @0 :SafetyModel;
safetyParam @3 :UInt16;
safetyParamDEPRECATED @1 :Int16;
safetyParam2DEPRECATED @2 :UInt32;
}
struct LateralParams {
torqueBP @0 :List(Int32);
torqueV @1 :List(Int32);
}
struct LateralPIDTuning {
kpBP @0 :List(Float32);
@@ -352,21 +575,41 @@ struct CarParams {
kf @4 :Float32;
}
struct LateralTorqueTuning {
useSteeringAngle @0 :Bool;
kp @1 :Float32;
ki @2 :Float32;
friction @3 :Float32;
kf @4 :Float32;
steeringAngleDeadzoneDeg @5 :Float32;
latAccelFactor @6 :Float32;
latAccelOffset @7 :Float32;
}
struct LongitudinalPIDTuning {
kpBP @0 :List(Float32);
kpV @1 :List(Float32);
kiBP @2 :List(Float32);
kiV @3 :List(Float32);
kf @6 :Float32;
deadzoneBP @4 :List(Float32);
deadzoneV @5 :List(Float32);
}
struct LateralINDITuning {
outerLoopGain @0 :Float32;
innerLoopGain @1 :Float32;
timeConstant @2 :Float32;
actuatorEffectiveness @3 :Float32;
outerLoopGainBP @4 :List(Float32);
outerLoopGainV @5 :List(Float32);
innerLoopGainBP @6 :List(Float32);
innerLoopGainV @7 :List(Float32);
timeConstantBP @8 :List(Float32);
timeConstantV @9 :List(Float32);
actuatorEffectivenessBP @10 :List(Float32);
actuatorEffectivenessV @11 :List(Float32);
outerLoopGainDEPRECATED @0 :Float32;
innerLoopGainDEPRECATED @1 :Float32;
timeConstantDEPRECATED @2 :Float32;
actuatorEffectivenessDEPRECATED @3 :Float32;
}
struct LateralLQRTuning {
@@ -383,25 +626,132 @@ struct CarParams {
l @7 :List(Float32); # Kalman gain
}
enum SafetyModel {
# does NOT match board setting
noOutput @0;
honda @1;
silent @0;
hondaNidec @1;
toyota @2;
elm327 @3;
gm @4;
hondaBosch @5;
hondaBoschGiraffe @5;
ford @6;
cadillac @7;
hyundai @8;
chrysler @9;
tesla @10;
subaru @11;
gmPassive @12;
mazda @13;
nissan @14;
volkswagen @15;
toyotaIpas @16;
allOutput @17;
gmAscm @18;
noOutput @19; # like silent but without silent CAN TXs
hondaBosch @20;
volkswagenPq @21;
subaruPreglobal @22; # pre-Global platform
hyundaiLegacy @23;
hyundaiCommunity @24;
volkswagenMlb @25;
hongqi @26;
body @27;
hyundaiCanfd @28;
volkswagenMqbEvo @29;
chryslerCusw @30;
psa @31;
}
enum SteerControlType {
torque @0;
angle @1;
curvatureDEPRECATED @2;
}
enum TransmissionType {
unknown @0;
automatic @1; # Traditional auto, including DSG
manual @2; # True "stick shift" only
direct @3; # Electric vehicle or other direct drive
cvt @4;
}
struct CarFw {
ecu @0 :Ecu;
fwVersion @1 :Data;
address @2 :UInt32;
subAddress @3 :UInt8;
responseAddress @4 :UInt32;
request @5 :List(Data);
brand @6 :Text;
bus @7 :UInt8;
logging @8 :Bool;
obdMultiplexing @9 :Bool;
}
enum Ecu {
eps @0;
abs @1;
fwdRadar @2;
fwdCamera @3;
engine @4;
unknown @5;
transmission @8; # Transmission Control Module
hybrid @18; # hybrid control unit, e.g. Chrysler's HCP, Honda's IMA Control Unit, Toyota's hybrid control computer
srs @9; # airbag
gateway @10; # can gateway
hud @11; # heads up display
combinationMeter @12; # instrument cluster
electricBrakeBooster @15;
shiftByWire @16;
adas @19;
cornerRadar @21;
hvac @20;
parkingAdas @7; # parking assist system ECU, e.g. Toyota's IPAS, Hyundai's RSPA, etc.
epb @22; # electronic parking brake
telematics @23;
body @24; # body control module
# Toyota only
dsu @6;
# Honda only
vsa @13; # Vehicle Stability Assist
programmedFuelInjection @14;
debug @17;
}
enum FingerprintSource {
can @0;
fw @1;
fixed @2;
}
enum NetworkLocation {
fwdCamera @0; # Standard/default integration at LKAS camera
gateway @1; # Integration at vehicle's CAN gateway
}
enableGasInterceptor @2 :Bool;
enableCameraDEPRECATED @4 :Bool;
enableApgsDEPRECATED @6 :Bool;
steerRateCostDEPRECATED @33 :Float32;
isPandaBlackDEPRECATED @39 :Bool;
hasStockCameraDEPRECATED @57 :Bool;
safetyParamDEPRECATED @10 :Int16;
safetyModelDEPRECATED @9 :SafetyModel;
safetyModelPassiveDEPRECATED @42 :SafetyModel = silent;
minSpeedCanDEPRECATED @51 :Float32;
communityFeatureDEPRECATED @46: Bool;
startingAccelRateDEPRECATED @53 :Float32;
steerMaxBPDEPRECATED @11 :List(Float32);
steerMaxVDEPRECATED @12 :List(Float32);
gasMaxBPDEPRECATED @13 :List(Float32);
gasMaxVDEPRECATED @14 :List(Float32);
brakeMaxBPDEPRECATED @15 :List(Float32);
brakeMaxVDEPRECATED @16 :List(Float32);
directAccelControlDEPRECATED @30 :Bool;
maxSteeringAngleDegDEPRECATED @54 :Float32;
longitudinalActuatorDelayLowerBoundDEPRECATEDDEPRECATED @61 :Float32;
}
+102
View File
@@ -0,0 +1,102 @@
using Cxx = import "./include/c++.capnp";
$Cxx.namespace("cereal");
using Car = import "car.capnp";
@0xb526ba661d550a59;
# custom.capnp: a home for empty structs reserved for custom forks
# These structs are guaranteed to remain reserved and empty in mainline
# cereal, so use these if you want custom events in your fork.
# you can rename the struct, but don't change the identifier
struct FrogPilotCarParams @0x81c2f05a394cf4af {
fpFlags @0 :UInt32;
openpilotLongitudinalControlDisabled @1 :Bool;
}
struct FrogPilotCarState @0xaedffd8f31e7b55d {
struct ButtonEvent {
enum Type {
lkas @0;
}
}
accelPressed @0 :Bool;
alwaysOnLateralAllowed @1 :Bool;
alwaysOnLateralEnabled @2 :Bool;
brakeLights @3 :Bool;
dashboardSpeedLimit @4 :Float32;
decelPressed @5 :Bool;
distancePressed @6 :Bool;
distanceLongPressed @7 :Bool;
distanceVeryLongPressed @8 :Bool;
ecoGear @9 :Bool;
forceCoast @10 :Bool;
pauseLateral @11 :Bool;
pauseLongitudinal @12 :Bool;
sportGear @13 :Bool;
trafficMode @14 :Bool;
}
struct FrogPilotDeviceState @0xf35cc4560bbf6ec2 {
freeSpace @0 :Int16;
usedSpace @1 :Int16;
}
struct FrogPilotNavigation @0xda96579883444c35 {
approachingIntersection @0 :Bool;
approachingTurn @1 :Bool;
navigationSpeedLimit @2 :Float32;
}
struct FrogPilotPlan @0x80ae746ee2596b11 {
accelerationJerk @0 :Float32;
accelerationJerkStock @1 :Float32;
dangerJerk @2 :Float32;
desiredFollowDistance @3 :Int64;
experimentalMode @4 :Bool;
forcingStop @5 :Bool;
forcingStopLength @6 :Float32;
frogpilotEvents @7 :List(Car.CarEvent);
lateralCheck @8 :Bool;
laneWidthLeft @9 :Float32;
laneWidthRight @10 :Float32;
maxAcceleration @11 :Float32;
minAcceleration @12 :Float32;
mtscSpeed @13 :Float32;
redLight @14 :Bool;
roadCurvature @15 :Float32;
slcMapSpeedLimit @16 :Float32;
slcMapboxSpeedLimit @17 :Float32;
slcNextSpeedLimit @18 :Float32;
slcOverridden @19 :Bool;
slcOverriddenSpeed @20 :Float32;
slcSpeedLimit @21 :Float32;
slcSpeedLimitOffset @22 :Float32;
slcSpeedLimitSource @23 :Text;
speedJerk @24 :Float32;
speedJerkStock @25 :Float32;
speedLimitChanged @26 :Bool;
tFollow @27 :Float32;
togglesUpdated @28 :Bool;
unconfirmedSlcSpeedLimit @29 :Float32;
vCruise @30 :Float32;
vtscControllingCurve @31 :Bool;
vtscSpeed @32 :Float32;
}
struct CustomReserved5 @0xa5cd762cd951a455 {
}
struct CustomReserved6 @0xf98d843bfd7004a3 {
}
struct CustomReserved7 @0xb86e6369214c01c8 {
}
struct CustomReserved8 @0xf416ec09499d9d19 {
}
struct CustomReserved9 @0xa1680744031fdb2d {
}
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-26
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@@ -1,26 +0,0 @@
#!/bin/bash
rm -r gen/ts
rm -r gen/js
mkdir gen/ts
mkdir gen/js
echo "Installing needed npm modules"
npm i capnpc-ts capnp-ts
capnpc -o node_modules/.bin/capnpc-ts:gen/ts log.capnp car.capnp
capnpc -o node_modules/.bin/capnpc-ts:gen/ts car.capnp
cat log.capnp | egrep '\([a-zA-Z]*\.[^\s]+\.[^s]+\)' | sed 's/^.*([a-zA-Z]*\.\([a-zA-Z.]*\)).*/\1/' | while read line
do
TOKEN=`echo $line | sed 's/\./_/g'`
ROOT=`echo $line | sed 's/\..*$//g'`
cat gen/ts/log.capnp.ts | grep '^import.*'${TOKEN}
if [[ "$?" == "1" ]]
then
sed -i 's/^\(import {.*\)'${ROOT}'\(,*\) \(.*\)$/\1'${ROOT}', '${TOKEN}'\2 \3/' ./gen/ts/log.capnp.ts
fi
done
tsc ./gen/ts/* --lib es2015 --outDir ./gen/js
-28
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@@ -1,28 +0,0 @@
# Copyright (c) 2013-2015 Sandstorm Development Group, Inc. and contributors
# Licensed under the MIT License:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
@0xc5f1af96651f70ea;
annotation package @0x9ee4c8f803b3b596 (file) : Text;
# Name of the package, such as "org.example.foo", in which the generated code will reside.
annotation outerClassname @0x9b066bb4881f7cd3 (file) : Text;
# Name of the outer class that will wrap the generated code.
-39
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@@ -1,39 +0,0 @@
set -e
echo "Installing capnp"
cd /tmp
VERSION=0.6.1
wget https://capnproto.org/capnproto-c++-${VERSION}.tar.gz
tar xvf capnproto-c++-${VERSION}.tar.gz
cd capnproto-c++-${VERSION}
CXXFLAGS="-fPIC" ./configure
make -j4
# manually build binaries statically
g++ -std=gnu++11 -I./src -I./src -DKJ_HEADER_WARNINGS -DCAPNP_HEADER_WARNINGS -DCAPNP_INCLUDE_DIR=\"/usr/local/include\" -pthread -O2 -DNDEBUG -pthread -pthread -o .libs/capnp src/capnp/compiler/module-loader.o src/capnp/compiler/capnp.o ./.libs/libcapnpc.a ./.libs/libcapnp.a ./.libs/libkj.a -lpthread -pthread
g++ -std=gnu++11 -I./src -I./src -DKJ_HEADER_WARNINGS -DCAPNP_HEADER_WARNINGS -DCAPNP_INCLUDE_DIR=\"/usr/local/include\" -pthread -O2 -DNDEBUG -pthread -pthread -o .libs/capnpc-c++ src/capnp/compiler/capnpc-c++.o ./.libs/libcapnp.a ./.libs/libkj.a -lpthread -pthread
g++ -std=gnu++11 -I./src -I./src -DKJ_HEADER_WARNINGS -DCAPNP_HEADER_WARNINGS -DCAPNP_INCLUDE_DIR=\"/usr/local/include\" -pthread -O2 -DNDEBUG -pthread -pthread -o .libs/capnpc-capnp src/capnp/compiler/capnpc-capnp.o ./.libs/libcapnp.a ./.libs/libkj.a -lpthread -pthread
cp .libs/capnp /usr/local/bin/
ln -s /usr/local/bin/capnp /usr/local/bin/capnpc
cp .libs/capnpc-c++ /usr/local/bin/
cp .libs/capnpc-capnp /usr/local/bin/
cp .libs/*.a /usr/local/lib
cd /tmp
echo "Installing c-capnp"
git clone https://github.com/commaai/c-capnproto.git
cd c-capnproto
git submodule update --init --recursive
autoreconf -f -i -s
CXXFLAGS="-fPIC" ./configure
make -j4
# manually build binaries statically
gcc -fPIC -o .libs/capnpc-c compiler/capnpc-c.o compiler/schema.capnp.o compiler/str.o ./.libs/libcapnp_c.a
cp .libs/capnpc-c /usr/local/bin/
cp .libs/*.a /usr/local/lib
+574
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@@ -0,0 +1,574 @@
using Cxx = import "./include/c++.capnp";
$Cxx.namespace("cereal");
@0x80ef1ec4889c2a63;
# legacy.capnp: a home for deprecated structs
struct LogRotate @0x9811e1f38f62f2d1 {
segmentNum @0 :Int32;
path @1 :Text;
}
struct LiveUI @0xc08240f996aefced {
rearViewCam @0 :Bool;
alertText1 @1 :Text;
alertText2 @2 :Text;
awarenessStatus @3 :Float32;
}
struct UiLayoutState @0x88dcce08ad29dda0 {
activeApp @0 :App;
sidebarCollapsed @1 :Bool;
mapEnabled @2 :Bool;
mockEngaged @3 :Bool;
enum App @0x9917470acf94d285 {
home @0;
music @1;
nav @2;
settings @3;
none @4;
}
}
struct OrbslamCorrection @0x8afd33dc9b35e1aa {
correctionMonoTime @0 :UInt64;
prePositionECEF @1 :List(Float64);
postPositionECEF @2 :List(Float64);
prePoseQuatECEF @3 :List(Float32);
postPoseQuatECEF @4 :List(Float32);
numInliers @5 :UInt32;
}
struct EthernetPacket @0xa99a9d5b33cf5859 {
pkt @0 :Data;
ts @1 :Float32;
}
struct CellInfo @0xcff7566681c277ce {
timestamp @0 :UInt64;
repr @1 :Text; # android toString() for now
}
struct WifiScan @0xd4df5a192382ba0b {
bssid @0 :Text;
ssid @1 :Text;
capabilities @2 :Text;
frequency @3 :Int32;
level @4 :Int32;
timestamp @5 :Int64;
centerFreq0 @6 :Int32;
centerFreq1 @7 :Int32;
channelWidth @8 :ChannelWidth;
operatorFriendlyName @9 :Text;
venueName @10 :Text;
is80211mcResponder @11 :Bool;
passpoint @12 :Bool;
distanceCm @13 :Int32;
distanceSdCm @14 :Int32;
enum ChannelWidth @0xcb6a279f015f6b51 {
w20Mhz @0;
w40Mhz @1;
w80Mhz @2;
w160Mhz @3;
w80Plus80Mhz @4;
}
}
struct LiveEventData @0x94b7baa90c5c321e {
name @0 :Text;
value @1 :Int32;
}
struct ModelData @0xb8aad62cffef28a9 {
frameId @0 :UInt32;
frameAge @12 :UInt32;
frameDropPerc @13 :Float32;
timestampEof @9 :UInt64;
modelExecutionTime @14 :Float32;
gpuExecutionTime @16 :Float32;
rawPred @15 :Data;
path @1 :PathData;
leftLane @2 :PathData;
rightLane @3 :PathData;
lead @4 :LeadData;
freePath @6 :List(Float32);
settings @5 :ModelSettings;
leadFuture @7 :LeadData;
speed @8 :List(Float32);
meta @10 :MetaData;
longitudinal @11 :LongitudinalData;
struct PathData @0x8817eeea389e9f08 {
points @0 :List(Float32);
prob @1 :Float32;
std @2 :Float32;
stds @3 :List(Float32);
poly @4 :List(Float32);
validLen @5 :Float32;
}
struct LeadData @0xd1c9bef96d26fa91 {
dist @0 :Float32;
prob @1 :Float32;
std @2 :Float32;
relVel @3 :Float32;
relVelStd @4 :Float32;
relY @5 :Float32;
relYStd @6 :Float32;
relA @7 :Float32;
relAStd @8 :Float32;
}
struct ModelSettings @0xa26e3710efd3e914 {
bigBoxX @0 :UInt16;
bigBoxY @1 :UInt16;
bigBoxWidth @2 :UInt16;
bigBoxHeight @3 :UInt16;
boxProjection @4 :List(Float32);
yuvCorrection @5 :List(Float32);
inputTransform @6 :List(Float32);
}
struct MetaData @0x9744f25fb60f2bf8 {
engagedProb @0 :Float32;
desirePrediction @1 :List(Float32);
brakeDisengageProb @2 :Float32;
gasDisengageProb @3 :Float32;
steerOverrideProb @4 :Float32;
desireState @5 :List(Float32);
}
struct LongitudinalData @0xf98f999c6a071122 {
distances @2 :List(Float32);
speeds @0 :List(Float32);
accelerations @1 :List(Float32);
}
}
struct ECEFPoint @0xc25bbbd524983447 {
x @0 :Float64;
y @1 :Float64;
z @2 :Float64;
}
struct ECEFPointDEPRECATED @0xe10e21168db0c7f7 {
x @0 :Float32;
y @1 :Float32;
z @2 :Float32;
}
struct GPSPlannerPoints @0xab54c59699f8f9f3 {
curPosDEPRECATED @0 :ECEFPointDEPRECATED;
pointsDEPRECATED @1 :List(ECEFPointDEPRECATED);
curPos @6 :ECEFPoint;
points @7 :List(ECEFPoint);
valid @2 :Bool;
trackName @3 :Text;
speedLimit @4 :Float32;
accelTarget @5 :Float32;
}
struct GPSPlannerPlan @0xf5ad1d90cdc1dd6b {
valid @0 :Bool;
poly @1 :List(Float32);
trackName @2 :Text;
speed @3 :Float32;
acceleration @4 :Float32;
pointsDEPRECATED @5 :List(ECEFPointDEPRECATED);
points @6 :List(ECEFPoint);
xLookahead @7 :Float32;
}
struct UiNavigationEvent @0x90c8426c3eaddd3b {
type @0: Type;
status @1: Status;
distanceTo @2: Float32;
endRoadPointDEPRECATED @3: ECEFPointDEPRECATED;
endRoadPoint @4: ECEFPoint;
enum Type @0xe8db07dcf8fcea05 {
none @0;
laneChangeLeft @1;
laneChangeRight @2;
mergeLeft @3;
mergeRight @4;
turnLeft @5;
turnRight @6;
}
enum Status @0xb9aa88c75ef99a1f {
none @0;
passive @1;
approaching @2;
active @3;
}
}
struct LiveLocationData @0xb99b2bc7a57e8128 {
status @0 :UInt8;
# 3D fix
lat @1 :Float64;
lon @2 :Float64;
alt @3 :Float32; # m
# speed
speed @4 :Float32; # m/s
# NED velocity components
vNED @5 :List(Float32);
# roll, pitch, heading (x,y,z)
roll @6 :Float32; # WRT to center of earth?
pitch @7 :Float32; # WRT to center of earth?
heading @8 :Float32; # WRT to north?
# what are these?
wanderAngle @9 :Float32;
trackAngle @10 :Float32;
# car frame -- https://upload.wikimedia.org/wikipedia/commons/f/f5/RPY_angles_of_cars.png
# gyro, in car frame, deg/s
gyro @11 :List(Float32);
# accel, in car frame, m/s^2
accel @12 :List(Float32);
accuracy @13 :Accuracy;
source @14 :SensorSource;
# if we are fixing a location in the past
fixMonoTime @15 :UInt64;
gpsWeek @16 :Int32;
timeOfWeek @17 :Float64;
positionECEF @18 :List(Float64);
poseQuatECEF @19 :List(Float32);
pitchCalibration @20 :Float32;
yawCalibration @21 :Float32;
imuFrame @22 :List(Float32);
struct Accuracy @0x943dc4625473b03f {
pNEDError @0 :List(Float32);
vNEDError @1 :List(Float32);
rollError @2 :Float32;
pitchError @3 :Float32;
headingError @4 :Float32;
ellipsoidSemiMajorError @5 :Float32;
ellipsoidSemiMinorError @6 :Float32;
ellipsoidOrientationError @7 :Float32;
}
enum SensorSource @0xc871d3cc252af657 {
applanix @0;
kalman @1;
orbslam @2;
timing @3;
dummy @4;
}
}
struct OrbOdometry @0xd7700859ed1f5b76 {
# timing first
startMonoTime @0 :UInt64;
endMonoTime @1 :UInt64;
# fundamental matrix and error
f @2: List(Float64);
err @3: Float64;
# number of inlier points
inliers @4: Int32;
# for debug only
# indexed by endMonoTime features
# value is startMonoTime feature match
# -1 if no match
matches @5: List(Int16);
}
struct OrbFeatures @0xcd60164a8a0159ef {
timestampEof @0 :UInt64;
# transposed arrays of normalized image coordinates
# len(xs) == len(ys) == len(descriptors) * 32
xs @1 :List(Float32);
ys @2 :List(Float32);
descriptors @3 :Data;
octaves @4 :List(Int8);
# match index to last OrbFeatures
# -1 if no match
timestampLastEof @5 :UInt64;
matches @6: List(Int16);
}
struct OrbFeaturesSummary @0xd500d30c5803fa4f {
timestampEof @0 :UInt64;
timestampLastEof @1 :UInt64;
featureCount @2 :UInt16;
matchCount @3 :UInt16;
computeNs @4 :UInt64;
}
struct OrbKeyFrame @0xc8233c0345e27e24 {
# this is a globally unique id for the KeyFrame
id @0: UInt64;
# this is the location of the KeyFrame
pos @1: ECEFPoint;
# these are the features in the world
# len(dpos) == len(descriptors) * 32
dpos @2 :List(ECEFPoint);
descriptors @3 :Data;
}
struct KalmanOdometry @0x92e21bb7ea38793a {
trans @0 :List(Float32); # m/s in device frame
rot @1 :List(Float32); # rad/s in device frame
transStd @2 :List(Float32); # std m/s in device frame
rotStd @3 :List(Float32); # std rad/s in device frame
}
struct OrbObservation @0x9b326d4e436afec7 {
observationMonoTime @0 :UInt64;
normalizedCoordinates @1 :List(Float32);
locationECEF @2 :List(Float64);
matchDistance @3: UInt32;
}
struct CalibrationFeatures @0x8fdfadb254ea867a {
frameId @0 :UInt32;
p0 @1 :List(Float32);
p1 @2 :List(Float32);
status @3 :List(Int8);
}
struct NavStatus @0xbd8822120928120c {
isNavigating @0 :Bool;
currentAddress @1 :Address;
struct Address @0xce7cd672cacc7814 {
title @0 :Text;
lat @1 :Float64;
lng @2 :Float64;
house @3 :Text;
address @4 :Text;
street @5 :Text;
city @6 :Text;
state @7 :Text;
country @8 :Text;
}
}
struct NavUpdate @0xdb98be6565516acb {
isNavigating @0 :Bool;
curSegment @1 :Int32;
segments @2 :List(Segment);
struct LatLng @0x9eaef9187cadbb9b {
lat @0 :Float64;
lng @1 :Float64;
}
struct Segment @0xa5b39b4fc4d7da3f {
from @0 :LatLng;
to @1 :LatLng;
updateTime @2 :Int32;
distance @3 :Int32;
crossTime @4 :Int32;
exitNo @5 :Int32;
instruction @6 :Instruction;
parts @7 :List(LatLng);
enum Instruction @0xc5417a637451246f {
turnLeft @0;
turnRight @1;
keepLeft @2;
keepRight @3;
straight @4;
roundaboutExitNumber @5;
roundaboutExit @6;
roundaboutTurnLeft @7;
unkn8 @8;
roundaboutStraight @9;
unkn10 @10;
roundaboutTurnRight @11;
unkn12 @12;
roundaboutUturn @13;
unkn14 @14;
arrive @15;
exitLeft @16;
exitRight @17;
unkn18 @18;
uturn @19;
# ...
}
}
}
struct TrafficEvent @0xacfa74a094e62626 {
type @0 :Type;
distance @1 :Float32;
action @2 :Action;
resuming @3 :Bool;
enum Type @0xd85d75253435bf4b {
stopSign @0;
lightRed @1;
lightYellow @2;
lightGreen @3;
stopLight @4;
}
enum Action @0xa6f6ce72165ccb49 {
none @0;
yield @1;
stop @2;
resumeReady @3;
}
}
struct AndroidGnss @0xdfdf30d03fc485bd {
union {
measurements @0 :Measurements;
navigationMessage @1 :NavigationMessage;
}
struct Measurements @0xa20710d4f428d6cd {
clock @0 :Clock;
measurements @1 :List(Measurement);
struct Clock @0xa0e27b453a38f450 {
timeNanos @0 :Int64;
hardwareClockDiscontinuityCount @1 :Int32;
hasTimeUncertaintyNanos @2 :Bool;
timeUncertaintyNanos @3 :Float64;
hasLeapSecond @4 :Bool;
leapSecond @5 :Int32;
hasFullBiasNanos @6 :Bool;
fullBiasNanos @7 :Int64;
hasBiasNanos @8 :Bool;
biasNanos @9 :Float64;
hasBiasUncertaintyNanos @10 :Bool;
biasUncertaintyNanos @11 :Float64;
hasDriftNanosPerSecond @12 :Bool;
driftNanosPerSecond @13 :Float64;
hasDriftUncertaintyNanosPerSecond @14 :Bool;
driftUncertaintyNanosPerSecond @15 :Float64;
}
struct Measurement @0xd949bf717d77614d {
svId @0 :Int32;
constellation @1 :Constellation;
timeOffsetNanos @2 :Float64;
state @3 :Int32;
receivedSvTimeNanos @4 :Int64;
receivedSvTimeUncertaintyNanos @5 :Int64;
cn0DbHz @6 :Float64;
pseudorangeRateMetersPerSecond @7 :Float64;
pseudorangeRateUncertaintyMetersPerSecond @8 :Float64;
accumulatedDeltaRangeState @9 :Int32;
accumulatedDeltaRangeMeters @10 :Float64;
accumulatedDeltaRangeUncertaintyMeters @11 :Float64;
hasCarrierFrequencyHz @12 :Bool;
carrierFrequencyHz @13 :Float32;
hasCarrierCycles @14 :Bool;
carrierCycles @15 :Int64;
hasCarrierPhase @16 :Bool;
carrierPhase @17 :Float64;
hasCarrierPhaseUncertainty @18 :Bool;
carrierPhaseUncertainty @19 :Float64;
hasSnrInDb @20 :Bool;
snrInDb @21 :Float64;
multipathIndicator @22 :MultipathIndicator;
enum Constellation @0x9ef1f3ff0deb5ffb {
unknown @0;
gps @1;
sbas @2;
glonass @3;
qzss @4;
beidou @5;
galileo @6;
}
enum State @0xcbb9490adce12d72 {
unknown @0;
codeLock @1;
bitSync @2;
subframeSync @3;
towDecoded @4;
msecAmbiguous @5;
symbolSync @6;
gloStringSync @7;
gloTodDecoded @8;
bdsD2BitSync @9;
bdsD2SubframeSync @10;
galE1bcCodeLock @11;
galE1c2ndCodeLock @12;
galE1bPageSync @13;
sbasSync @14;
}
enum MultipathIndicator @0xc04e7b6231d4caa8 {
unknown @0;
detected @1;
notDetected @2;
}
}
}
struct NavigationMessage @0xe2517b083095fd4e {
type @0 :Int32;
svId @1 :Int32;
messageId @2 :Int32;
submessageId @3 :Int32;
data @4 :Data;
status @5 :Status;
enum Status @0xec1ff7996b35366f {
unknown @0;
parityPassed @1;
parityRebuilt @2;
}
}
}
struct LidarPts @0xe3d6685d4e9d8f7a {
r @0 :List(UInt16); # uint16 m*500.0
theta @1 :List(UInt16); # uint16 deg*100.0
reflect @2 :List(UInt8); # uint8 0-255
# For storing out of file.
idx @3 :UInt64;
# For storing in file
pkt @4 :Data;
}
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@@ -1,10 +1,6 @@
using Cxx = import "./include/c++.capnp";
$Cxx.namespace("cereal");
using Java = import "./include/java.capnp";
$Java.package("ai.comma.openpilot.cereal");
$Java.outerClassname("Map");
@0xa086df597ef5d7a0;
# Geometry
+10
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@@ -0,0 +1,10 @@
demo
bridge
test_runner
*.o
*.os
*.d
*.a
*.so
messaging_pyx.cpp
build/
+252
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@@ -0,0 +1,252 @@
# must be built with scons
from msgq.ipc_pyx import Context, Poller, SubSocket, PubSocket, SocketEventHandle, toggle_fake_events, \
set_fake_prefix, get_fake_prefix, delete_fake_prefix, wait_for_one_event
from msgq.ipc_pyx import MultiplePublishersError, IpcError
from msgq import fake_event_handle, pub_sock, sub_sock, drain_sock_raw, context
import os
import capnp
import time
from typing import Optional, List, Union, Dict, Deque
from collections import deque
from cereal import log
from cereal.services import SERVICE_LIST
NO_TRAVERSAL_LIMIT = 2**64-1
def log_from_bytes(dat: bytes) -> capnp.lib.capnp._DynamicStructReader:
with log.Event.from_bytes(dat, traversal_limit_in_words=NO_TRAVERSAL_LIMIT) as msg:
return msg
def new_message(service: Optional[str], size: Optional[int] = None, **kwargs) -> capnp.lib.capnp._DynamicStructBuilder:
args = {
'valid': False,
'logMonoTime': int(time.monotonic() * 1e9),
**kwargs
}
dat = log.Event.new_message(**args)
if service is not None:
if size is None:
dat.init(service)
else:
dat.init(service, size)
return dat
def drain_sock(sock: SubSocket, wait_for_one: bool = False) -> List[capnp.lib.capnp._DynamicStructReader]:
"""Receive all message currently available on the queue"""
msgs = drain_sock_raw(sock, wait_for_one=wait_for_one)
return [log_from_bytes(m) for m in msgs]
# TODO: print when we drop packets?
def recv_sock(sock: SubSocket, wait: bool = False) -> Optional[capnp.lib.capnp._DynamicStructReader]:
"""Same as drain sock, but only returns latest message. Consider using conflate instead."""
dat = None
while 1:
if wait and dat is None:
recv = sock.receive()
else:
recv = sock.receive(non_blocking=True)
if recv is None: # Timeout hit
break
dat = recv
if dat is not None:
dat = log_from_bytes(dat)
return dat
def recv_one(sock: SubSocket) -> Optional[capnp.lib.capnp._DynamicStructReader]:
dat = sock.receive()
if dat is not None:
dat = log_from_bytes(dat)
return dat
def recv_one_or_none(sock: SubSocket) -> Optional[capnp.lib.capnp._DynamicStructReader]:
dat = sock.receive(non_blocking=True)
if dat is not None:
dat = log_from_bytes(dat)
return dat
def recv_one_retry(sock: SubSocket) -> capnp.lib.capnp._DynamicStructReader:
"""Keep receiving until we get a message"""
while True:
dat = sock.receive()
if dat is not None:
return log_from_bytes(dat)
class SubMaster:
def __init__(self, services: List[str], poll: Optional[str] = None,
ignore_alive: Optional[List[str]] = None, ignore_avg_freq: Optional[List[str]] = None,
ignore_valid: Optional[List[str]] = None, addr: str = "127.0.0.1", frequency: Optional[float] = None):
self.frame = -1
self.seen = {s: False for s in services}
self.updated = {s: False for s in services}
self.recv_time = {s: 0. for s in services}
self.recv_frame = {s: 0 for s in services}
self.alive = {s: False for s in services}
self.freq_ok = {s: False for s in services}
self.recv_dts: Dict[str, Deque[float]] = {}
self.sock = {}
self.data = {}
self.valid = {}
self.logMonoTime = {}
self.max_freq = {}
self.min_freq = {}
self.poller = Poller()
polled_services = set([poll, ] if poll is not None else services)
self.non_polled_services = set(services) - polled_services
self.ignore_average_freq = [] if ignore_avg_freq is None else ignore_avg_freq
self.ignore_alive = [] if ignore_alive is None else ignore_alive
self.ignore_valid = [] if ignore_valid is None else ignore_valid
self.simulation = bool(int(os.getenv("SIMULATION", "0")))
# if freq and poll aren't specified, assume the max to be conservative
assert frequency is None or poll is None, "Do not specify 'frequency' - frequency of the polled service will be used."
self.update_freq = frequency or max([SERVICE_LIST[s].frequency for s in polled_services])
for s in services:
p = self.poller if s not in self.non_polled_services else None
self.sock[s] = sub_sock(s, poller=p, addr=addr, conflate=True)
try:
data = new_message(s)
except capnp.lib.capnp.KjException:
data = new_message(s, 0) # lists
self.data[s] = getattr(data.as_reader(), s)
self.logMonoTime[s] = 0
self.valid[s] = True # FIXME: this should default to False
freq = max(min([SERVICE_LIST[s].frequency, self.update_freq]), 1.)
if s == poll:
max_freq = freq
min_freq = freq
else:
max_freq = min(freq, self.update_freq)
if SERVICE_LIST[s].frequency >= 2*self.update_freq:
min_freq = self.update_freq
elif self.update_freq >= 2*SERVICE_LIST[s].frequency:
min_freq = freq
else:
min_freq = min(freq, freq / 2.)
self.max_freq[s] = max_freq*1.2
self.min_freq[s] = min_freq*0.8
self.recv_dts[s] = deque(maxlen=int(10*freq))
def __getitem__(self, s: str) -> capnp.lib.capnp._DynamicStructReader:
return self.data[s]
def _check_avg_freq(self, s: str) -> bool:
return SERVICE_LIST[s].frequency > 0.99 and (s not in self.ignore_average_freq) and (s not in self.ignore_alive)
def update(self, timeout: int = 100) -> None:
msgs = []
for sock in self.poller.poll(timeout):
msgs.append(recv_one_or_none(sock))
# non-blocking receive for non-polled sockets
for s in self.non_polled_services:
msgs.append(recv_one_or_none(self.sock[s]))
self.update_msgs(time.monotonic(), msgs)
def update_msgs(self, cur_time: float, msgs: List[capnp.lib.capnp._DynamicStructReader]) -> None:
self.frame += 1
self.updated = dict.fromkeys(self.updated, False)
for msg in msgs:
if msg is None:
continue
s = msg.which()
self.seen[s] = True
self.updated[s] = True
if self.recv_time[s] > 1e-5:
self.recv_dts[s].append(cur_time - self.recv_time[s])
self.recv_time[s] = cur_time
self.recv_frame[s] = self.frame
self.data[s] = getattr(msg, s)
self.logMonoTime[s] = msg.logMonoTime
self.valid[s] = msg.valid
for s in self.data:
if SERVICE_LIST[s].frequency > 1e-5 and not self.simulation:
# alive if delay is within 10x the expected frequency
self.alive[s] = (cur_time - self.recv_time[s]) < (10. / SERVICE_LIST[s].frequency)
# check average frequency; slow to fall, quick to recover
dts = self.recv_dts[s]
assert dts.maxlen is not None
recent_dts = list(dts)[-int(dts.maxlen / 10):]
try:
avg_freq = 1 / (sum(dts) / len(dts))
avg_freq_recent = 1 / (sum(recent_dts) / len(recent_dts))
except ZeroDivisionError:
avg_freq = 0
avg_freq_recent = 0
avg_freq_ok = self.min_freq[s] <= avg_freq <= self.max_freq[s]
recent_freq_ok = self.min_freq[s] <= avg_freq_recent <= self.max_freq[s]
self.freq_ok[s] = avg_freq_ok or recent_freq_ok
else:
self.freq_ok[s] = True
if self.simulation:
self.alive[s] = self.seen[s] # alive is defined as seen when simulation flag set
else:
self.alive[s] = True
def all_alive(self, service_list: Optional[List[str]] = None) -> bool:
if service_list is None:
service_list = list(self.sock.keys())
return all(self.alive[s] for s in service_list if s not in self.ignore_alive)
def all_freq_ok(self, service_list: Optional[List[str]] = None) -> bool:
if service_list is None:
service_list = list(self.sock.keys())
return all(self.freq_ok[s] for s in service_list if self._check_avg_freq(s))
def all_valid(self, service_list: Optional[List[str]] = None) -> bool:
if service_list is None:
service_list = list(self.sock.keys())
return all(self.valid[s] for s in service_list if s not in self.ignore_valid)
def all_checks(self, service_list: Optional[List[str]] = None) -> bool:
return self.all_alive(service_list) and self.all_freq_ok(service_list) and self.all_valid(service_list)
class PubMaster:
def __init__(self, services: List[str]):
self.sock = {}
for s in services:
self.sock[s] = pub_sock(s)
def send(self, s: str, dat: Union[bytes, capnp.lib.capnp._DynamicStructBuilder]) -> None:
if not isinstance(dat, bytes):
dat = dat.to_bytes()
self.sock[s].send(dat)
def wait_for_readers_to_update(self, s: str, timeout: int, dt: float = 0.05) -> bool:
for _ in range(int(timeout*(1./dt))):
if self.sock[s].all_readers_updated():
return True
time.sleep(dt)
return False
def all_readers_updated(self, s: str) -> bool:
return self.sock[s].all_readers_updated() # type: ignore
BIN
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+128
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@@ -0,0 +1,128 @@
#!/usr/bin/env python3
from typing import Optional
class Service:
def __init__(self, should_log: bool, frequency: float, decimation: Optional[int] = None):
self.should_log = should_log
self.frequency = frequency
self.decimation = decimation
_services: dict[str, tuple] = {
# service: (should_log, frequency, qlog decimation (optional))
# note: the "EncodeIdx" packets will still be in the log
"gyroscope": (True, 104., 104),
"gyroscope2": (True, 100., 100),
"accelerometer": (True, 104., 104),
"accelerometer2": (True, 100., 100),
"magnetometer": (True, 25., 25),
"lightSensor": (True, 100., 100),
"temperatureSensor": (True, 2., 200),
"temperatureSensor2": (True, 2., 200),
"gpsNMEA": (True, 9.),
"deviceState": (True, 2., 1),
"can": (True, 100., 1223), # decimation gives ~5 msgs in a full segment
"controlsState": (True, 100., 10),
"pandaStates": (True, 10., 1),
"peripheralState": (True, 2., 1),
"radarState": (True, 20., 5),
"roadEncodeIdx": (False, 20., 1),
"liveTracks": (True, 20.),
"sendcan": (True, 100., 139),
"logMessage": (True, 0.),
"errorLogMessage": (True, 0., 1),
"liveCalibration": (True, 4., 4),
"liveTorqueParameters": (True, 4., 1),
"androidLog": (True, 0.),
"carState": (True, 100., 10),
"carControl": (True, 100., 10),
"carOutput": (True, 100., 10),
"longitudinalPlan": (True, 20., 5),
"procLog": (True, 0.5, 15),
"gpsLocationExternal": (True, 10., 10),
"gpsLocation": (True, 1., 1),
"ubloxGnss": (True, 10.),
"qcomGnss": (True, 2.),
"gnssMeasurements": (True, 10., 10),
"clocks": (True, 0.1, 1),
"ubloxRaw": (True, 20.),
"liveLocationKalman": (True, 20., 5),
"liveParameters": (True, 20., 5),
"cameraOdometry": (True, 20., 5),
"thumbnail": (True, 0.2, 1),
"onroadEvents": (True, 1., 1),
"carParams": (True, 0.02, 1),
"roadCameraState": (True, 20., 20),
"driverCameraState": (True, 20., 20),
"driverEncodeIdx": (False, 20., 1),
"driverStateV2": (True, 20., 10),
"driverMonitoringState": (True, 20., 10),
"wideRoadEncodeIdx": (False, 20., 1),
"wideRoadCameraState": (True, 20., 20),
"drivingModelData": (True, 20., 10),
"modelV2": (True, 20., 40),
"managerState": (True, 2., 1),
"uploaderState": (True, 0., 1),
"navInstruction": (True, 1., 10),
"navRoute": (True, 0.),
"navThumbnail": (True, 0.),
"navModel": (True, 2., 4.),
"mapRenderState": (True, 2., 1.),
"uiPlan": (True, 20., 40.),
"qRoadEncodeIdx": (False, 20.),
"userFlag": (True, 0., 1),
"microphone": (True, 10., 10),
# debug
"uiDebug": (True, 0., 1),
"testJoystick": (True, 0.),
"roadEncodeData": (False, 20.),
"driverEncodeData": (False, 20.),
"wideRoadEncodeData": (False, 20.),
"qRoadEncodeData": (False, 20.),
"livestreamWideRoadEncodeIdx": (False, 20.),
"livestreamRoadEncodeIdx": (False, 20.),
"livestreamDriverEncodeIdx": (False, 20.),
"livestreamWideRoadEncodeData": (False, 20.),
"livestreamRoadEncodeData": (False, 20.),
"livestreamDriverEncodeData": (False, 20.),
"customReservedRawData0": (True, 0.),
"customReservedRawData1": (True, 0.),
"customReservedRawData2": (True, 0.),
# FrogPilot
"frogpilotCarParams": (True, 0.02, 1),
"frogpilotCarState": (True, 100., 10),
"frogpilotDeviceState": (True, 2., 1),
"frogpilotNavigation": (True, 1., 10),
"frogpilotPlan": (True, 20., 5),
}
SERVICE_LIST = {name: Service(*vals) for
idx, (name, vals) in enumerate(_services.items())}
def build_header():
h = ""
h += "/* THIS IS AN AUTOGENERATED FILE, PLEASE EDIT services.py */\n"
h += "#ifndef __SERVICES_H\n"
h += "#define __SERVICES_H\n"
h += "#include <map>\n"
h += "#include <string>\n"
h += "struct service { std::string name; bool should_log; int frequency; int decimation; };\n"
h += "static std::map<std::string, service> services = {\n"
for k, v in SERVICE_LIST.items():
should_log = "true" if v.should_log else "false"
decimation = -1 if v.decimation is None else v.decimation
h += ' { "%s", {"%s", %s, %d, %d}},\n' % \
(k, k, should_log, v.frequency, decimation)
h += "};\n"
h += "#endif\n"
return h
if __name__ == "__main__":
print(build_header())
-11
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@@ -1,11 +0,0 @@
#!/bin/bash
pyflakes $(find . -iname "*.py" | grep -vi "^\./pyextra.*" | grep -vi "^\./panda")
RESULT=$?
if [ $RESULT -eq 0 ]; then
pylint $(find . -iname "*.py" | grep -vi "^\./pyextra.*" | grep -vi "^\./panda")
RESULT=$? & 3
fi
[ $RESULT -ne 0 ] && exit 1
exit 0
+1
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@@ -0,0 +1 @@
*.cpp
+27 -12
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@@ -1,13 +1,20 @@
import jwt
import os
import requests
from datetime import datetime, timedelta
from openpilot.system.hardware.hw import Paths
from openpilot.system.version import get_version
from selfdrive.version import version
from openpilot.frogpilot.common.frogpilot_utilities import use_konik_server
class Api(object):
def __init__(self, dongle_id, private_key):
API_HOST = os.getenv('API_HOST', 'https://api.commadotai.com')
KONIK_API_HOST = os.getenv('API_HOST', 'https://api.konik.ai')
class Api:
def __init__(self, dongle_id):
self.dongle_id = dongle_id
self.private_key = private_key
with open(Paths.persist_root()+'/comma/id_rsa') as f:
self.private_key = f.read()
def get(self, *args, **kwargs):
return self.request('GET', *args, **kwargs)
@@ -18,17 +25,25 @@ class Api(object):
def request(self, method, endpoint, timeout=None, access_token=None, **params):
return api_get(endpoint, method=method, timeout=timeout, access_token=access_token, **params)
def get_token(self):
return jwt.encode({'identity': self.dongle_id, 'exp': datetime.utcnow() + timedelta(hours=1)}, self.private_key, algorithm='RS256')
def get_token(self, expiry_hours=1):
now = datetime.utcnow()
payload = {
'identity': self.dongle_id,
'nbf': now,
'iat': now,
'exp': now + timedelta(hours=expiry_hours)
}
token = jwt.encode(payload, self.private_key, algorithm='RS256')
if isinstance(token, bytes):
token = token.decode('utf8')
return token
def api_get(endpoint, method='GET', timeout=None, access_token=None, **params):
backend = "https://api.commadotai.com/"
headers = {}
if access_token is not None:
headers['Authorization'] = "JWT "+access_token
headers['Authorization'] = "JWT " + access_token
headers['User-Agent'] = "openpilot-" + version
return requests.request(method, backend+endpoint, timeout=timeout, headers = headers, params=params)
headers['User-Agent'] = "openpilot-" + get_version()
return requests.request(method, (KONIK_API_HOST if use_konik_server() else API_HOST) + "/" + endpoint, timeout=timeout, headers=headers, params=params)
+2 -2
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@@ -1,4 +1,4 @@
import os
BASEDIR = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../"))
-16
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@@ -1,16 +0,0 @@
from posix.time cimport clock_gettime, timespec, CLOCK_BOOTTIME, CLOCK_MONOTONIC_RAW
cdef double readclock(int clock_id):
cdef timespec ts
cdef double current
clock_gettime(clock_id, &ts)
current = ts.tv_sec + (ts.tv_nsec / 1000000000.)
return current
def monotonic_time():
return readclock(CLOCK_MONOTONIC_RAW)
def sec_since_boot():
return readclock(CLOCK_BOOTTIME)
+25
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@@ -0,0 +1,25 @@
import numpy as np
class Conversions:
# Speed
MPH_TO_KPH = 1.609344
KPH_TO_MPH = 1. / MPH_TO_KPH
MS_TO_KPH = 3.6
KPH_TO_MS = 1. / MS_TO_KPH
MS_TO_MPH = MS_TO_KPH * KPH_TO_MPH
MPH_TO_MS = MPH_TO_KPH * KPH_TO_MS
MS_TO_KNOTS = 1.9438
KNOTS_TO_MS = 1. / MS_TO_KNOTS
# Distance
METER_TO_FOOT = 3.28084
FOOT_TO_METER = 1. / METER_TO_FOOT
CM_TO_INCH = 1. / 2.54
INCH_TO_CM = 1. / CM_TO_INCH
# Angle
DEG_TO_RAD = np.pi / 180.
RAD_TO_DEG = 1. / DEG_TO_RAD
# Mass
LB_TO_KG = 0.453592
-289
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@@ -1,289 +0,0 @@
import re
import os
import struct
import sys
import numbers
from collections import namedtuple, defaultdict
def int_or_float(s):
# return number, trying to maintain int format
if s.isdigit():
return int(s, 10)
else:
return float(s)
DBCSignal = namedtuple(
"DBCSignal", ["name", "start_bit", "size", "is_little_endian", "is_signed",
"factor", "offset", "tmin", "tmax", "units"])
class dbc(object):
def __init__(self, fn):
self.name, _ = os.path.splitext(os.path.basename(fn))
with open(fn) as f:
self.txt = f.readlines()
self._warned_addresses = set()
# regexps from https://github.com/ebroecker/canmatrix/blob/master/canmatrix/importdbc.py
bo_regexp = re.compile(r"^BO\_ (\w+) (\w+) *: (\w+) (\w+)")
sg_regexp = re.compile(r"^SG\_ (\w+) : (\d+)\|(\d+)@(\d+)([\+|\-]) \(([0-9.+\-eE]+),([0-9.+\-eE]+)\) \[([0-9.+\-eE]+)\|([0-9.+\-eE]+)\] \"(.*)\" (.*)")
sgm_regexp = re.compile(r"^SG\_ (\w+) (\w+) *: (\d+)\|(\d+)@(\d+)([\+|\-]) \(([0-9.+\-eE]+),([0-9.+\-eE]+)\) \[([0-9.+\-eE]+)\|([0-9.+\-eE]+)\] \"(.*)\" (.*)")
val_regexp = re.compile(r"VAL\_ (\w+) (\w+) (\s*[-+]?[0-9]+\s+\".+?\"[^;]*)")
# A dictionary which maps message ids to tuples ((name, size), signals).
# name is the ASCII name of the message.
# size is the size of the message in bytes.
# signals is a list signals contained in the message.
# signals is a list of DBCSignal in order of increasing start_bit.
self.msgs = {}
# A dictionary which maps message ids to a list of tuples (signal name, definition value pairs)
self.def_vals = defaultdict(list)
# lookup to bit reverse each byte
self.bits_index = [(i & ~0b111) + ((-i-1) & 0b111) for i in xrange(64)]
for l in self.txt:
l = l.strip()
if l.startswith("BO_ "):
# new group
dat = bo_regexp.match(l)
if dat is None:
print("bad BO {0}".format(l))
name = dat.group(2)
size = int(dat.group(3))
ids = int(dat.group(1), 0) # could be hex
if ids in self.msgs:
sys.exit("Duplicate address detected %d %s" % (ids, self.name))
self.msgs[ids] = ((name, size), [])
if l.startswith("SG_ "):
# new signal
dat = sg_regexp.match(l)
go = 0
if dat is None:
dat = sgm_regexp.match(l)
go = 1
if dat is None:
print("bad SG {0}".format(l))
sgname = dat.group(1)
start_bit = int(dat.group(go+2))
signal_size = int(dat.group(go+3))
is_little_endian = int(dat.group(go+4))==1
is_signed = dat.group(go+5)=='-'
factor = int_or_float(dat.group(go+6))
offset = int_or_float(dat.group(go+7))
tmin = int_or_float(dat.group(go+8))
tmax = int_or_float(dat.group(go+9))
units = dat.group(go+10)
self.msgs[ids][1].append(
DBCSignal(sgname, start_bit, signal_size, is_little_endian,
is_signed, factor, offset, tmin, tmax, units))
if l.startswith("VAL_ "):
# new signal value/definition
dat = val_regexp.match(l)
if dat is None:
print("bad VAL {0}".format(l))
ids = int(dat.group(1), 0) # could be hex
sgname = dat.group(2)
defvals = dat.group(3)
defvals = defvals.replace("?",r"\?") #escape sequence in C++
defvals = defvals.split('"')[:-1]
defs = defvals[1::2]
#cleanup, convert to UPPER_CASE_WITH_UNDERSCORES
for i,d in enumerate(defs):
d = defs[i].strip().upper()
defs[i] = d.replace(" ","_")
defvals[1::2] = defs
defvals = '"'+"".join(str(i) for i in defvals)+'"'
self.def_vals[ids].append((sgname, defvals))
for msg in self.msgs.values():
msg[1].sort(key=lambda x: x.start_bit)
self.msg_name_to_address = {}
for address, m in self.msgs.items():
name = m[0][0]
self.msg_name_to_address[name] = address
def lookup_msg_id(self, msg_id):
if not isinstance(msg_id, numbers.Number):
msg_id = self.msg_name_to_address[msg_id]
return msg_id
def reverse_bytes(self, x):
return ((x & 0xff00000000000000) >> 56) | \
((x & 0x00ff000000000000) >> 40) | \
((x & 0x0000ff0000000000) >> 24) | \
((x & 0x000000ff00000000) >> 8) | \
((x & 0x00000000ff000000) << 8) | \
((x & 0x0000000000ff0000) << 24) | \
((x & 0x000000000000ff00) << 40) | \
((x & 0x00000000000000ff) << 56)
def encode(self, msg_id, dd):
"""Encode a CAN message using the dbc.
Inputs:
msg_id: The message ID.
dd: A dictionary mapping signal name to signal data.
"""
msg_id = self.lookup_msg_id(msg_id)
msg_def = self.msgs[msg_id]
size = msg_def[0][1]
result = 0
for s in msg_def[1]:
ival = dd.get(s.name)
if ival is not None:
b2 = s.size
if s.is_little_endian:
b1 = s.start_bit
else:
b1 = (s.start_bit // 8) * 8 + (-s.start_bit - 1) % 8
bo = 64 - (b1 + s.size)
ival = (ival / s.factor) - s.offset
ival = int(round(ival))
if s.is_signed and ival < 0:
ival = (1 << b2) + ival
shift = b1 if s.is_little_endian else bo
mask = ((1 << b2) - 1) << shift
dat = (ival & ((1 << b2) - 1)) << shift
if s.is_little_endian:
mask = self.reverse_bytes(mask)
dat = self.reverse_bytes(dat)
result &= ~mask
result |= dat
result = struct.pack('>Q', result)
return result[:size]
def decode(self, x, arr=None, debug=False):
"""Decode a CAN message using the dbc.
Inputs:
x: A collection with elements (address, time, data), where address is
the CAN address, time is the bus time, and data is the CAN data as a
hex string.
arr: Optional list of signals which should be decoded and returned.
debug: True to print debugging statements.
Returns:
A tuple (name, data), where name is the name of the CAN message and data
is the decoded result. If arr is None, data is a dict of properties.
Otherwise data is a list of the same length as arr.
Returns (None, None) if the message could not be decoded.
"""
if arr is None:
out = {}
else:
out = [None]*len(arr)
msg = self.msgs.get(x[0])
if msg is None:
if x[0] not in self._warned_addresses:
#print("WARNING: Unknown message address {}".format(x[0]))
self._warned_addresses.add(x[0])
return None, None
name = msg[0][0]
if debug:
print(name)
st = x[2].ljust(8, '\x00')
le, be = None, None
for s in msg[1]:
if arr is not None and s[0] not in arr:
continue
start_bit = s[1]
signal_size = s[2]
little_endian = s[3]
signed = s[4]
factor = s[5]
offset = s[6]
b2 = signal_size
if little_endian:
b1 = start_bit
else:
b1 = (start_bit // 8) * 8 + (-start_bit - 1) % 8
bo = 64 - (b1 + signal_size)
if little_endian:
if le is None:
le = struct.unpack("<Q", st)[0]
shift_amount = b1
tmp = le
else:
if be is None:
be = struct.unpack(">Q", st)[0]
shift_amount = bo
tmp = be
if shift_amount < 0:
continue
tmp = (tmp >> shift_amount) & ((1 << b2) - 1)
if signed and (tmp >> (b2 - 1)):
tmp -= (1 << b2)
tmp = tmp * factor + offset
# if debug:
# print("%40s %2d %2d %7.2f %s" % (s[0], s[1], s[2], tmp, s[-1]))
if arr is None:
out[s[0]] = tmp
else:
out[arr.index(s[0])] = tmp
return name, out
def get_signals(self, msg):
msg = self.lookup_msg_id(msg)
return [sgs.name for sgs in self.msgs[msg][1]]
if __name__ == "__main__":
from opendbc import DBC_PATH
import numpy as np
dbc_test = dbc(os.path.join(DBC_PATH, 'toyota_prius_2017_pt_generated.dbc'))
msg = ('STEER_ANGLE_SENSOR', {'STEER_ANGLE': -6.0, 'STEER_RATE': 4, 'STEER_FRACTION': -0.2})
encoded = dbc_test.encode(*msg)
decoded = dbc_test.decode((0x25, 0, encoded))
assert decoded == msg
dbc_test = dbc(os.path.join(DBC_PATH, 'hyundai_santa_fe_2019_ccan.dbc'))
decoded = dbc_test.decode((0x2b0, 0, "\xfa\xfe\x00\x07\x12"))
assert np.isclose(decoded[1]['SAS_Angle'], -26.2)
msg = ('SAS11', {'SAS_Stat': 7.0, 'MsgCount': 0.0, 'SAS_Angle': -26.200000000000003, 'SAS_Speed': 0.0, 'CheckSum': 0.0})
encoded = dbc_test.encode(*msg)
decoded = dbc_test.decode((0x2b0, 0, encoded))
assert decoded == msg
+9
View File
@@ -0,0 +1,9 @@
# remove all keys that end in DEPRECATED
def strip_deprecated_keys(d):
for k in list(d.keys()):
if isinstance(k, str):
if k.endswith('DEPRECATED'):
d.pop(k)
elif isinstance(d[k], dict):
strip_deprecated_keys(d[k])
return d
+6 -47
View File
@@ -1,49 +1,8 @@
import os
import sys
import fcntl
import hashlib
from cffi import FFI
import platform
def ffi_wrap(name, c_code, c_header, tmpdir="/tmp/ccache", cflags="", libraries=None):
if libraries is None:
libraries = []
cache = name + "_" + hashlib.sha1(c_code).hexdigest()
try:
os.mkdir(tmpdir)
except OSError:
pass
fd = os.open(tmpdir, 0)
fcntl.flock(fd, fcntl.LOCK_EX)
try:
sys.path.append(tmpdir)
try:
mod = __import__(cache)
except Exception:
print("cache miss {0}".format(cache))
compile_code(cache, c_code, c_header, tmpdir, cflags, libraries)
mod = __import__(cache)
finally:
os.close(fd)
return mod.ffi, mod.lib
def compile_code(name, c_code, c_header, directory, cflags="", libraries=None):
if libraries is None:
libraries = []
ffibuilder = FFI()
ffibuilder.set_source(name, c_code, source_extension='.cpp', libraries=libraries)
ffibuilder.cdef(c_header)
os.environ['OPT'] = "-fwrapv -O2 -DNDEBUG -std=c++11"
os.environ['CFLAGS'] = cflags
ffibuilder.compile(verbose=True, debug=False, tmpdir=directory)
def wrap_compiled(name, directory):
sys.path.append(directory)
mod = __import__(name)
return mod.ffi, mod.lib
def suffix():
if platform.system() == "Darwin":
return ".dylib"
else:
return ".so"
+30 -111
View File
@@ -1,118 +1,37 @@
import os
import shutil
import tempfile
from atomicwrites import AtomicWriter
def mkdirs_exists_ok(path):
try:
os.makedirs(path)
except OSError:
if not os.path.isdir(path):
raise
def rm_not_exists_ok(path):
try:
os.remove(path)
except OSError:
if os.path.exists(path):
raise
def rm_tree_or_link(path):
if os.path.islink(path):
os.unlink(path)
elif os.path.isdir(path):
shutil.rmtree(path)
def get_tmpdir_on_same_filesystem(path):
# TODO(mgraczyk): HACK, we should actually check for which filesystem.
normpath = os.path.normpath(path)
parts = normpath.split("/")
if len(parts) > 1:
if parts[1].startswith("raid") or parts[1].startswith("datasets"):
if len(parts) > 2 and parts[2] == "runner":
return "/{}/runner/tmp".format(parts[1])
elif len(parts) > 2 and parts[2] == "aws":
return "/{}/aws/tmp".format(parts[1])
else:
return "/{}/tmp".format(parts[1])
elif parts[1] == "aws":
return "/aws/tmp"
elif parts[1] == "scratch":
return "/scratch/tmp"
return "/tmp"
class AutoMoveTempdir(object):
def __init__(self, target_path, temp_dir=None):
self._target_path = target_path
self._path = tempfile.mkdtemp(dir=temp_dir)
@property
def name(self):
return self._path
def close(self):
os.rename(self._path, self._target_path)
def __enter__(self): return self
def __exit__(self, type, value, traceback):
if type is None:
self.close()
else:
shutil.rmtree(self._path)
class NamedTemporaryDir(object):
def __init__(self, temp_dir=None):
self._path = tempfile.mkdtemp(dir=temp_dir)
@property
def name(self):
return self._path
def close(self):
shutil.rmtree(self._path)
def __enter__(self): return self
def __exit__(self, type, value, traceback):
self.close()
def _get_fileobject_func(writer, temp_dir):
def _get_fileobject():
file_obj = writer.get_fileobject(dir=temp_dir)
os.chmod(file_obj.name, 0o644)
return file_obj
return _get_fileobject
def atomic_write_on_fs_tmp(path, **kwargs):
"""Creates an atomic writer using a temporary file in a temporary directory
on the same filesystem as path.
"""
# TODO(mgraczyk): This use of AtomicWriter relies on implementation details to set the temp
# directory.
writer = AtomicWriter(path, **kwargs)
return writer._open(_get_fileobject_func(writer, get_tmpdir_on_same_filesystem(path)))
import contextlib
def atomic_write_in_dir(path, **kwargs):
"""Creates an atomic writer using a temporary file in the same directory
as the destination file.
"""
writer = AtomicWriter(path, **kwargs)
return writer._open(_get_fileobject_func(writer, os.path.dirname(path)))
class CallbackReader:
"""Wraps a file, but overrides the read method to also
call a callback function with the number of bytes read so far."""
def __init__(self, f, callback, *args):
self.f = f
self.callback = callback
self.cb_args = args
self.total_read = 0
def atomic_write_in_dir_neos(path, contents, mode=None):
"""
Atomically writes contents to path using a temporary file in the same directory
as path. Useful on NEOS, where `os.link` (required by atomic_write_in_dir) is missing.
"""
def __getattr__(self, attr):
return getattr(self.f, attr)
f = tempfile.NamedTemporaryFile(delete=False, prefix=".tmp", dir=os.path.dirname(path))
f.write(contents)
f.flush()
if mode is not None:
os.fchmod(f.fileno(), mode)
os.fsync(f.fileno())
f.close()
def read(self, *args, **kwargs):
chunk = self.f.read(*args, **kwargs)
self.total_read += len(chunk)
self.callback(*self.cb_args, self.total_read)
return chunk
os.rename(f.name, path)
@contextlib.contextmanager
def atomic_write_in_dir(path: str, mode: str = 'w', buffering: int = -1, encoding: str = None, newline: str = None,
overwrite: bool = False):
"""Write to a file atomically using a temporary file in the same directory as the destination file."""
dir_name = os.path.dirname(path)
if not overwrite and os.path.exists(path):
raise FileExistsError(f"File '{path}' already exists. To overwrite it, set 'overwrite' to True.")
with tempfile.NamedTemporaryFile(mode=mode, buffering=buffering, encoding=encoding, newline=newline, dir=dir_name, delete=False) as tmp_file:
yield tmp_file
tmp_file_name = tmp_file.name
os.replace(tmp_file_name, path)
+14 -6
View File
@@ -1,10 +1,18 @@
class FirstOrderFilter():
class FirstOrderFilter:
# first order filter
def __init__(self, x0, ts, dt):
self.k = (dt / ts) / (1. + dt / ts)
def __init__(self, x0, rc, dt, initialized=True):
self.x = x0
self.dt = dt
self.update_alpha(rc)
self.initialized = initialized
def update_alpha(self, rc):
self.alpha = self.dt / (rc + self.dt)
def update(self, x):
self.x = (1. - self.k) * self.x + self.k * x
if self.initialized:
self.x = (1. - self.alpha) * self.x + self.alpha * x
else:
self.initialized = True
self.x = x
return self.x
-62
View File
@@ -1,62 +0,0 @@
import os
from common.basedir import BASEDIR
def get_fingerprint_list():
# read all the folders in selfdrive/car and return a dict where:
# - keys are all the car models for which we have a fingerprint
# - values are lists dicts of messages that constitute the unique
# CAN fingerprint of each car model and all its variants
fingerprints = {}
for car_folder in [x[0] for x in os.walk(BASEDIR + '/selfdrive/car')]:
try:
car_name = car_folder.split('/')[-1]
values = __import__('selfdrive.car.%s.values' % car_name, fromlist=['FINGERPRINTS'])
if hasattr(values, 'FINGERPRINTS'):
car_fingerprints = values.FINGERPRINTS
else:
continue
for f, v in car_fingerprints.items():
fingerprints[f] = v
except (ImportError, IOError):
pass
return fingerprints
_FINGERPRINTS = get_fingerprint_list()
_DEBUG_ADDRESS = {1880: 8} # reserved for debug purposes
def is_valid_for_fingerprint(msg, car_fingerprint):
adr = msg.address
# ignore addresses that are more than 11 bits
return (adr in car_fingerprint and car_fingerprint[adr] == len(msg.dat)) or adr >= 0x800
def eliminate_incompatible_cars(msg, candidate_cars):
"""Removes cars that could not have sent msg.
Inputs:
msg: A cereal/log CanData message from the car.
candidate_cars: A list of cars to consider.
Returns:
A list containing the subset of candidate_cars that could have sent msg.
"""
compatible_cars = []
for car_name in candidate_cars:
car_fingerprints = _FINGERPRINTS[car_name]
for fingerprint in car_fingerprints:
fingerprint.update(_DEBUG_ADDRESS) # add alien debug address
if is_valid_for_fingerprint(msg, fingerprint):
compatible_cars.append(car_name)
break
return compatible_cars
def all_known_cars():
"""Returns a list of all known car strings."""
return list(_FINGERPRINTS.keys())
+42
View File
@@ -0,0 +1,42 @@
from functools import cache
import subprocess
from openpilot.common.run import run_cmd, run_cmd_default
@cache
def get_commit(cwd: str = None, branch: str = "HEAD") -> str:
return run_cmd_default(["git", "rev-parse", branch], cwd=cwd)
@cache
def get_commit_date(cwd: str = None, commit: str = "HEAD") -> str:
return run_cmd_default(["git", "show", "--no-patch", "--format='%ct %ci'", commit], cwd=cwd)
@cache
def get_short_branch(cwd: str = None) -> str:
return run_cmd_default(["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd=cwd)
@cache
def get_branch(cwd: str = None) -> str:
return run_cmd_default(["git", "rev-parse", "--abbrev-ref", "--symbolic-full-name", "@{u}"], cwd=cwd)
@cache
def get_origin(cwd: str = None) -> str:
try:
local_branch = run_cmd(["git", "name-rev", "--name-only", "HEAD"], cwd=cwd)
tracking_remote = run_cmd(["git", "config", "branch." + local_branch + ".remote"], cwd=cwd)
return run_cmd(["git", "config", "remote." + tracking_remote + ".url"], cwd=cwd)
except subprocess.CalledProcessError: # Not on a branch, fallback
return run_cmd_default(["git", "config", "--get", "remote.origin.url"], cwd=cwd)
@cache
def get_normalized_origin(cwd: str = None) -> str:
return get_origin(cwd) \
.replace("git@", "", 1) \
.replace(".git", "", 1) \
.replace("https://", "", 1) \
.replace(":", "/", 1)
+54
View File
@@ -0,0 +1,54 @@
import os
from functools import cache
def gpio_init(pin: int, output: bool) -> None:
try:
with open(f"/sys/class/gpio/gpio{pin}/direction", 'wb') as f:
f.write(b"out" if output else b"in")
except Exception as e:
print(f"Failed to set gpio {pin} direction: {e}")
def gpio_set(pin: int, high: bool) -> None:
try:
with open(f"/sys/class/gpio/gpio{pin}/value", 'wb') as f:
f.write(b"1" if high else b"0")
except Exception as e:
print(f"Failed to set gpio {pin} value: {e}")
def gpio_read(pin: int) -> bool | None:
val = None
try:
with open(f"/sys/class/gpio/gpio{pin}/value", 'rb') as f:
val = bool(int(f.read().strip()))
except Exception as e:
print(f"Failed to set gpio {pin} value: {e}")
return val
def gpio_export(pin: int) -> None:
if os.path.isdir(f"/sys/class/gpio/gpio{pin}"):
return
try:
with open("/sys/class/gpio/export", 'w') as f:
f.write(str(pin))
except Exception:
print(f"Failed to export gpio {pin}")
@cache
def get_irq_action(irq: int) -> list[str]:
try:
with open(f"/sys/kernel/irq/{irq}/actions") as f:
actions = f.read().strip().split(',')
return actions
except FileNotFoundError:
return []
def get_irqs_for_action(action: str) -> list[str]:
ret = []
with open("/proc/interrupts") as f:
for l in f.readlines():
irq = l.split(':')[0].strip()
if irq.isdigit() and action in get_irq_action(irq):
ret.append(irq)
return ret
-10
View File
@@ -1,10 +0,0 @@
all: simple_kalman_impl.so
simple_kalman_impl.so: simple_kalman_impl.pyx simple_kalman_impl.pxd simple_kalman_setup.py
python2 simple_kalman_setup.py build_ext --inplace
rm -rf build
rm simple_kalman_impl.c
.PHONY: clean
clean:
rm -f simple_kalman_impl.so
-10
View File
@@ -1,10 +0,0 @@
# pylint: skip-file
import os
import subprocess
kalman_dir = os.path.dirname(os.path.abspath(__file__))
subprocess.check_call(["make", "simple_kalman_impl.so"], cwd=kalman_dir)
from simple_kalman_impl import KF1D as KF1D
# Silence pyflakes
assert KF1D
-16
View File
@@ -1,16 +0,0 @@
cdef class KF1D:
cdef public:
double x0_0
double x1_0
double K0_0
double K1_0
double A0_0
double A0_1
double A1_0
double A1_1
double C0_0
double C0_1
double A_K_0
double A_K_1
double A_K_2
double A_K_3
-35
View File
@@ -1,35 +0,0 @@
cdef class KF1D:
def __init__(self, x0, A, C, K):
self.x0_0 = x0[0][0]
self.x1_0 = x0[1][0]
self.A0_0 = A[0][0]
self.A0_1 = A[0][1]
self.A1_0 = A[1][0]
self.A1_1 = A[1][1]
self.C0_0 = C[0]
self.C0_1 = C[1]
self.K0_0 = K[0][0]
self.K1_0 = K[1][0]
self.A_K_0 = self.A0_0 - self.K0_0 * self.C0_0
self.A_K_1 = self.A0_1 - self.K0_0 * self.C0_1
self.A_K_2 = self.A1_0 - self.K1_0 * self.C0_0
self.A_K_3 = self.A1_1 - self.K1_0 * self.C0_1
def update(self, meas):
cdef double x0_0 = self.A_K_0 * self.x0_0 + self.A_K_1 * self.x1_0 + self.K0_0 * meas
cdef double x1_0 = self.A_K_2 * self.x0_0 + self.A_K_3 * self.x1_0 + self.K1_0 * meas
self.x0_0 = x0_0
self.x1_0 = x1_0
return [self.x0_0, self.x1_0]
@property
def x(self):
return [[self.x0_0], [self.x1_0]]
@x.setter
def x(self, x):
self.x0_0 = x[0][0]
self.x1_0 = x[1][0]
-23
View File
@@ -1,23 +0,0 @@
import numpy as np
class KF1D:
# this EKF assumes constant covariance matrix, so calculations are much simpler
# the Kalman gain also needs to be precomputed using the control module
def __init__(self, x0, A, C, K):
self.x = x0
self.A = A
self.C = C
self.K = K
self.A_K = self.A - np.dot(self.K, self.C)
# K matrix needs to be pre-computed as follow:
# import control
# (x, l, K) = control.dare(np.transpose(self.A), np.transpose(self.C), Q, R)
# self.K = np.transpose(K)
def update(self, meas):
self.x = np.dot(self.A_K, self.x) + np.dot(self.K, meas)
return self.x
-5
View File
@@ -1,5 +0,0 @@
from distutils.core import setup, Extension
from Cython.Build import cythonize
setup(name='Simple Kalman Implementation',
ext_modules=cythonize(Extension("simple_kalman_impl", ["simple_kalman_impl.pyx"])))
-85
View File
@@ -1,85 +0,0 @@
import unittest
import random
import timeit
import numpy as np
from common.kalman.simple_kalman import KF1D
from common.kalman.simple_kalman_old import KF1D as KF1D_old
class TestSimpleKalman(unittest.TestCase):
def setUp(self):
dt = 0.01
x0_0 = 0.0
x1_0 = 0.0
A0_0 = 1.0
A0_1 = dt
A1_0 = 0.0
A1_1 = 1.0
C0_0 = 1.0
C0_1 = 0.0
K0_0 = 0.12287673
K1_0 = 0.29666309
self.kf_old = KF1D_old(x0=np.matrix([[x0_0], [x1_0]]),
A=np.matrix([[A0_0, A0_1], [A1_0, A1_1]]),
C=np.matrix([C0_0, C0_1]),
K=np.matrix([[K0_0], [K1_0]]))
self.kf = KF1D(x0=[[x0_0], [x1_0]],
A=[[A0_0, A0_1], [A1_0, A1_1]],
C=[C0_0, C0_1],
K=[[K0_0], [K1_0]])
def test_getter_setter(self):
self.kf.x = [[1.0], [1.0]]
self.assertEqual(self.kf.x, [[1.0], [1.0]])
def update_returns_state(self):
x = self.kf.update(100)
self.assertEqual(x, self.kf.x)
def test_old_equal_new(self):
for _ in range(1000):
v_wheel = random.uniform(0, 200)
x_old = self.kf_old.update(v_wheel)
x = self.kf.update(v_wheel)
# Compare the output x, verify that the error is less than 1e-4
self.assertAlmostEqual(x_old[0], x[0])
self.assertAlmostEqual(x_old[1], x[1])
def test_new_is_faster(self):
setup = """
import numpy as np
from common.kalman.simple_kalman import KF1D
from common.kalman.simple_kalman_old import KF1D as KF1D_old
dt = 0.01
x0_0 = 0.0
x1_0 = 0.0
A0_0 = 1.0
A0_1 = dt
A1_0 = 0.0
A1_1 = 1.0
C0_0 = 1.0
C0_1 = 0.0
K0_0 = 0.12287673
K1_0 = 0.29666309
kf_old = KF1D_old(x0=np.matrix([[x0_0], [x1_0]]),
A=np.matrix([[A0_0, A0_1], [A1_0, A1_1]]),
C=np.matrix([C0_0, C0_1]),
K=np.matrix([[K0_0], [K1_0]]))
kf = KF1D(x0=[[x0_0], [x1_0]],
A=[[A0_0, A0_1], [A1_0, A1_1]],
C=[C0_0, C0_1],
K=[[K0_0], [K1_0]])
"""
kf_speed = timeit.timeit("kf.update(1234)", setup=setup, number=10000)
kf_old_speed = timeit.timeit("kf_old.update(1234)", setup=setup, number=10000)
self.assertTrue(kf_speed < kf_old_speed / 4)
+102 -29
View File
@@ -1,13 +1,19 @@
import io
import os
import sys
import copy
import json
import time
import uuid
import socket
import logging
import traceback
from threading import local
from collections import OrderedDict
from contextlib import contextmanager
LOG_TIMESTAMPS = "LOG_TIMESTAMPS" in os.environ
def json_handler(obj):
# if isinstance(obj, (datetime.date, datetime.time)):
# return obj.isoformat()
@@ -60,14 +66,57 @@ class SwagFormatter(logging.Formatter):
return record_dict
def format(self, record):
if self.swaglogger is None:
raise Exception("must set swaglogger before calling format()")
return json_robust_dumps(self.format_dict(record))
class SwagLogFileFormatter(SwagFormatter):
def fix_kv(self, k, v):
# append type to names to preserve legacy naming in logs
# avoids overlapping key namespaces with different types
# e.g. log.info() creates 'msg' -> 'msg$s'
# log.event() creates 'msg.health.logMonoTime' -> 'msg.health.logMonoTime$i'
# because overlapping namespace 'msg' caused problems
if isinstance(v, (str, bytes)):
k += "$s"
elif isinstance(v, float):
k += "$f"
elif isinstance(v, bool):
k += "$b"
elif isinstance(v, int):
k += "$i"
elif isinstance(v, dict):
nv = {}
for ik, iv in v.items():
ik, iv = self.fix_kv(ik, iv)
nv[ik] = iv
v = nv
elif isinstance(v, list):
k += "$a"
return k, v
def format(self, record):
if isinstance(record, str):
v = json.loads(record)
else:
v = self.format_dict(record)
mk, mv = self.fix_kv('msg', v['msg'])
del v['msg']
v[mk] = mv
v['id'] = uuid.uuid4().hex
return json_robust_dumps(v)
class SwagErrorFilter(logging.Filter):
def filter(self, record):
return record.levelno < logging.ERROR
_tmpfunc = lambda: 0
_srcfile = os.path.normcase(_tmpfunc.__code__.co_filename)
def _tmpfunc():
return 0
def _srcfile():
return os.path.normcase(_tmpfunc.__code__.co_filename)
class SwagLogger(logging.Logger):
def __init__(self):
@@ -78,28 +127,6 @@ class SwagLogger(logging.Logger):
self.log_local = local()
self.log_local.ctx = {}
def findCaller(self, stack_info=None):
"""
Find the stack frame of the caller so that we can note the source
file name, line number and function name.
"""
# f = currentframe()
f = sys._getframe(3)
#On some versions of IronPython, currentframe() returns None if
#IronPython isn't run with -X:Frames.
if f is not None:
f = f.f_back
rv = "(unknown file)", 0, "(unknown function)"
while hasattr(f, "f_code"):
co = f.f_code
filename = os.path.normcase(co.co_filename)
if filename in (logging._srcfile, _srcfile):
f = f.f_back
continue
rv = (co.co_filename, f.f_lineno, co.co_name)
break
return rv
def local_ctx(self):
try:
return self.log_local.ctx
@@ -126,20 +153,66 @@ class SwagLogger(logging.Logger):
def bind_global(self, **kwargs):
self.global_ctx.update(kwargs)
def event(self, event_name, *args, **kwargs):
def event(self, event, *args, **kwargs):
evt = NiceOrderedDict()
evt['event'] = event_name
evt['event'] = event
if args:
evt['args'] = args
evt.update(kwargs)
ctx = self.get_ctx()
if ctx:
evt['ctx'] = self.get_ctx()
if 'error' in kwargs:
self.error(evt)
elif 'debug' in kwargs:
self.debug(evt)
else:
self.info(evt)
def timestamp(self, event_name):
if LOG_TIMESTAMPS:
t = time.monotonic()
tstp = NiceOrderedDict()
tstp['timestamp'] = NiceOrderedDict()
tstp['timestamp']["event"] = event_name
tstp['timestamp']["time"] = t*1e9
self.debug(tstp)
def findCaller(self, stack_info=False, stacklevel=1):
"""
Find the stack frame of the caller so that we can note the source
file name, line number and function name.
"""
f = sys._getframe(3)
#On some versions of IronPython, currentframe() returns None if
#IronPython isn't run with -X:Frames.
if f is not None:
f = f.f_back
orig_f = f
while f and stacklevel > 1:
f = f.f_back
stacklevel -= 1
if not f:
f = orig_f
rv = "(unknown file)", 0, "(unknown function)", None
while hasattr(f, "f_code"):
co = f.f_code
filename = os.path.normcase(co.co_filename)
# TODO: is this pylint exception correct?
if filename == _srcfile:
f = f.f_back
continue
sinfo = None
if stack_info:
sio = io.StringIO()
sio.write('Stack (most recent call last):\n')
traceback.print_stack(f, file=sio)
sinfo = sio.getvalue()
if sinfo[-1] == '\n':
sinfo = sinfo[:-1]
sio.close()
rv = (co.co_filename, f.f_lineno, co.co_name, sinfo)
break
return rv
if __name__ == "__main__":
log = SwagLogger()
+45
View File
@@ -0,0 +1,45 @@
HTML_REPLACEMENTS = [
(r'&', r'&amp;'),
(r'"', r'&quot;'),
]
def parse_markdown(text: str, tab_length: int = 2) -> str:
lines = text.split("\n")
output: list[str] = []
list_level = 0
def end_outstanding_lists(level: int, end_level: int) -> int:
while level > end_level:
level -= 1
output.append("</ul>")
if level > 0:
output.append("</li>")
return end_level
for i, line in enumerate(lines):
if i + 1 < len(lines) and lines[i + 1].startswith("==="): # heading
output.append(f"<h1>{line}</h1>")
elif line.startswith("==="):
pass
elif line.lstrip().startswith("* "): # list
line_level = 1 + line.count(" " * tab_length, 0, line.index("*"))
if list_level >= line_level:
list_level = end_outstanding_lists(list_level, line_level)
else:
list_level += 1
if list_level > 1:
output[-1] = output[-1].replace("</li>", "")
output.append("<ul>")
output.append(f"<li>{line.replace('*', '', 1).lstrip()}</li>")
else:
list_level = end_outstanding_lists(list_level, 0)
if len(line) > 0:
output.append(line)
end_outstanding_lists(list_level, 0)
output_str = "\n".join(output) + "\n"
for (fr, to) in HTML_REPLACEMENTS:
output_str = output_str.replace(fr, to)
return output_str
+50
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@@ -0,0 +1,50 @@
"""
Utilities for generating mock messages for testing.
example in common/tests/test_mock.py
"""
import functools
import threading
from cereal.messaging import PubMaster
from cereal.services import SERVICE_LIST
from openpilot.common.mock.generators import generate_liveLocationKalman
from openpilot.common.realtime import Ratekeeper
MOCK_GENERATOR = {
"liveLocationKalman": generate_liveLocationKalman
}
def generate_messages_loop(services: list[str], done: threading.Event):
pm = PubMaster(services)
rk = Ratekeeper(100)
i = 0
while not done.is_set():
for s in services:
should_send = i % (100/SERVICE_LIST[s].frequency) == 0
if should_send:
message = MOCK_GENERATOR[s]()
pm.send(s, message)
i += 1
rk.keep_time()
def mock_messages(services: list[str] | str):
if isinstance(services, str):
services = [services]
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
done = threading.Event()
t = threading.Thread(target=generate_messages_loop, args=(services, done))
t.start()
try:
return func(*args, **kwargs)
finally:
done.set()
t.join()
return wrapper
return decorator
+20
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@@ -0,0 +1,20 @@
from cereal import messaging
LOCATION1 = (32.7174, -117.16277)
LOCATION2 = (32.7558, -117.2037)
LLK_DECIMATION = 10
RENDER_FRAMES = 15
DEFAULT_ITERATIONS = RENDER_FRAMES * LLK_DECIMATION
def generate_liveLocationKalman(location=LOCATION1):
msg = messaging.new_message('liveLocationKalman')
msg.liveLocationKalman.positionGeodetic = {'value': [*location, 0], 'std': [0., 0., 0.], 'valid': True}
msg.liveLocationKalman.positionECEF = {'value': [0., 0., 0.], 'std': [0., 0., 0.], 'valid': True}
msg.liveLocationKalman.calibratedOrientationNED = {'value': [0., 0., 0.], 'std': [0., 0., 0.], 'valid': True}
msg.liveLocationKalman.velocityCalibrated = {'value': [0., 0., 0.], 'std': [0., 0., 0.], 'valid': True}
msg.liveLocationKalman.status = 'valid'
msg.liveLocationKalman.gpsOK = True
return msg
+7 -6
View File
@@ -1,18 +1,19 @@
def int_rnd(x):
return int(round(x))
def clip(x, lo, hi):
return max(lo, min(hi, x))
def interp(x, xp, fp):
N = len(xp)
def get_interp(xv):
hi = 0
while hi < N and xv > xp[hi]:
hi += 1
low = hi - 1
return fp[-1] if hi == N and xv > xp[low] else (
fp[0] if hi == 0 else
fp[0] if hi == 0 else
(xv - xp[low]) * (fp[hi] - fp[low]) / (xp[hi] - xp[low]) + fp[low])
return [get_interp(v) for v in x] if hasattr(
x, '__iter__') else get_interp(x)
return [get_interp(v) for v in x] if hasattr(x, '__iter__') else get_interp(x)
def mean(x):
return sum(x) / len(x)
Executable → Regular
+15 -362
View File
@@ -1,365 +1,18 @@
#!/usr/bin/env python
"""ROS has a parameter server, we have files.
The parameter store is a persistent key value store, implemented as a directory with a writer lock.
On Android, we store params under params_dir = /data/params. The writer lock is a file
"<params_dir>/.lock" taken using flock(), and data is stored in a directory symlinked to by
"<params_dir>/d".
Each key, value pair is stored as a file with named <key> with contents <value>, located in
<params_dir>/d/<key>
Readers of a single key can just open("<params_dir>/d/<key>") and read the file contents.
Readers who want a consistent snapshot of multiple keys should take the lock.
Writers should take the lock before modifying anything. Writers should also leave the DB in a
consistent state after a crash. The implementation below does this by copying all params to a temp
directory <params_dir>/<tmp>, then atomically symlinking <params_dir>/<d> to <params_dir>/<tmp>
before deleting the old <params_dir>/<d> directory.
Writers that only modify a single key can simply take the lock, then swap the corresponding value
file in place without messing with <params_dir>/d.
"""
import time
import os
import errno
import sys
import shutil
import fcntl
import tempfile
from enum import Enum
def mkdirs_exists_ok(path):
try:
os.makedirs(path)
except OSError:
if not os.path.isdir(path):
raise
class TxType(Enum):
PERSISTENT = 1
CLEAR_ON_MANAGER_START = 2
CLEAR_ON_PANDA_DISCONNECT = 3
class UnknownKeyName(Exception):
pass
keys = {
"AccessToken": [TxType.PERSISTENT],
"AthenadPid": [TxType.PERSISTENT],
"CalibrationParams": [TxType.PERSISTENT],
"CarParams": [TxType.CLEAR_ON_MANAGER_START, TxType.CLEAR_ON_PANDA_DISCONNECT],
"CarVin": [TxType.CLEAR_ON_MANAGER_START, TxType.CLEAR_ON_PANDA_DISCONNECT],
"CompletedTrainingVersion": [TxType.PERSISTENT],
"ControlsParams": [TxType.PERSISTENT],
"DoUninstall": [TxType.CLEAR_ON_MANAGER_START],
"DongleId": [TxType.PERSISTENT],
"GithubSshKeys": [TxType.PERSISTENT],
"GitBranch": [TxType.PERSISTENT],
"GitCommit": [TxType.PERSISTENT],
"GitRemote": [TxType.PERSISTENT],
"HasAcceptedTerms": [TxType.PERSISTENT],
"IsDriverMonitoringEnabled": [TxType.PERSISTENT],
"IsFcwEnabled": [TxType.PERSISTENT],
"IsGeofenceEnabled": [TxType.PERSISTENT],
"IsMetric": [TxType.PERSISTENT],
"IsUpdateAvailable": [TxType.PERSISTENT],
"IsUploadRawEnabled": [TxType.PERSISTENT],
"IsUploadVideoOverCellularEnabled": [TxType.PERSISTENT],
"LimitSetSpeed": [TxType.PERSISTENT],
"LimitSetSpeedNeural": [TxType.PERSISTENT],
"LiveParameters": [TxType.PERSISTENT],
"LongitudinalControl": [TxType.PERSISTENT],
"Passive": [TxType.PERSISTENT],
"RecordFront": [TxType.PERSISTENT],
"ShouldDoUpdate": [TxType.CLEAR_ON_MANAGER_START],
"SpeedLimitOffset": [TxType.PERSISTENT],
"SubscriberInfo": [TxType.PERSISTENT],
"TermsVersion": [TxType.PERSISTENT],
"TrainingVersion": [TxType.PERSISTENT],
"Version": [TxType.PERSISTENT],
}
def fsync_dir(path):
fd = os.open(path, os.O_RDONLY)
try:
os.fsync(fd)
finally:
os.close(fd)
class FileLock(object):
def __init__(self, path, create):
self._path = path
self._create = create
self._fd = None
def acquire(self):
self._fd = os.open(self._path, os.O_CREAT if self._create else 0)
fcntl.flock(self._fd, fcntl.LOCK_EX)
def release(self):
if self._fd is not None:
os.close(self._fd)
self._fd = None
class DBAccessor(object):
def __init__(self, path):
self._path = path
self._vals = None
def keys(self):
self._check_entered()
return self._vals.keys()
def get(self, key):
self._check_entered()
try:
return self._vals[key]
except KeyError:
return None
def _get_lock(self, create):
lock = FileLock(os.path.join(self._path, ".lock"), create)
lock.acquire()
return lock
def _read_values_locked(self):
"""Callers should hold a lock while calling this method."""
vals = {}
try:
data_path = self._data_path()
keys = os.listdir(data_path)
for key in keys:
with open(os.path.join(data_path, key), "rb") as f:
vals[key] = f.read()
except (OSError, IOError) as e:
# Either the DB hasn't been created yet, or somebody wrote a bug and left the DB in an
# inconsistent state. Either way, return empty.
if e.errno == errno.ENOENT:
return {}
return vals
def _data_path(self):
return os.path.join(self._path, "d")
def _check_entered(self):
if self._vals is None:
raise Exception("Must call __enter__ before using DB")
class DBReader(DBAccessor):
def __enter__(self):
try:
lock = self._get_lock(False)
except OSError as e:
# Do not create lock if it does not exist.
if e.errno == errno.ENOENT:
self._vals = {}
return self
try:
# Read everything.
self._vals = self._read_values_locked()
return self
finally:
lock.release()
def __exit__(self, type, value, traceback): pass
class DBWriter(DBAccessor):
def __init__(self, path):
super(DBWriter, self).__init__(path)
self._lock = None
self._prev_umask = None
def put(self, key, value):
self._vals[key] = value
def delete(self, key):
self._vals.pop(key, None)
def __enter__(self):
mkdirs_exists_ok(self._path)
# Make sure we can write and that permissions are correct.
self._prev_umask = os.umask(0)
try:
os.chmod(self._path, 0o777)
self._lock = self._get_lock(True)
self._vals = self._read_values_locked()
except:
os.umask(self._prev_umask)
self._prev_umask = None
raise
return self
def __exit__(self, type, value, traceback):
self._check_entered()
try:
# data_path refers to the externally used path to the params. It is a symlink.
# old_data_path is the path currently pointed to by data_path.
# tempdir_path is a path where the new params will go, which the new data path will point to.
# new_data_path is a temporary symlink that will atomically overwrite data_path.
#
# The current situation is:
# data_path -> old_data_path
# We're going to write params data to tempdir_path
# tempdir_path -> params data
# Then point new_data_path to tempdir_path
# new_data_path -> tempdir_path
# Then atomically overwrite data_path with new_data_path
# data_path -> tempdir_path
old_data_path = None
new_data_path = None
tempdir_path = tempfile.mkdtemp(prefix=".tmp", dir=self._path)
try:
# Write back all keys.
os.chmod(tempdir_path, 0o777)
for k, v in self._vals.items():
with open(os.path.join(tempdir_path, k), "wb") as f:
f.write(v)
f.flush()
os.fsync(f.fileno())
fsync_dir(tempdir_path)
data_path = self._data_path()
try:
old_data_path = os.path.join(self._path, os.readlink(data_path))
except (OSError, IOError):
# NOTE(mgraczyk): If other DB implementations have bugs, this could cause
# copies to be left behind, but we still want to overwrite.
pass
new_data_path = "{}.link".format(tempdir_path)
os.symlink(os.path.basename(tempdir_path), new_data_path)
os.rename(new_data_path, data_path)
fsync_dir(self._path)
finally:
# If the rename worked, we can delete the old data. Otherwise delete the new one.
success = new_data_path is not None and os.path.exists(data_path) and (
os.readlink(data_path) == os.path.basename(tempdir_path))
if success:
if old_data_path is not None:
shutil.rmtree(old_data_path)
else:
shutil.rmtree(tempdir_path)
# Regardless of what happened above, there should be no link at new_data_path.
if new_data_path is not None and os.path.islink(new_data_path):
os.remove(new_data_path)
finally:
os.umask(self._prev_umask)
self._prev_umask = None
# Always release the lock.
self._lock.release()
self._lock = None
def read_db(params_path, key):
path = "%s/d/%s" % (params_path, key)
try:
with open(path, "rb") as f:
return f.read()
except IOError:
return None
def write_db(params_path, key, value):
prev_umask = os.umask(0)
lock = FileLock(params_path+"/.lock", True)
lock.acquire()
try:
tmp_path = tempfile.mktemp(prefix=".tmp", dir=params_path)
with open(tmp_path, "wb") as f:
f.write(value)
f.flush()
os.fsync(f.fileno())
path = "%s/d/%s" % (params_path, key)
os.rename(tmp_path, path)
fsync_dir(os.path.dirname(path))
finally:
os.umask(prev_umask)
lock.release()
class Params(object):
def __init__(self, db='/data/params'):
self.db = db
# create the database if it doesn't exist...
if not os.path.exists(self.db+"/d"):
with self.transaction(write=True):
pass
def transaction(self, write=False):
if write:
return DBWriter(self.db)
else:
return DBReader(self.db)
def _clear_keys_with_type(self, tx_type):
with self.transaction(write=True) as txn:
for key in keys:
if tx_type in keys[key]:
txn.delete(key)
def manager_start(self):
self._clear_keys_with_type(TxType.CLEAR_ON_MANAGER_START)
def panda_disconnect(self):
self._clear_keys_with_type(TxType.CLEAR_ON_PANDA_DISCONNECT)
def delete(self, key):
with self.transaction(write=True) as txn:
txn.delete(key)
def get(self, key, block=False):
if key not in keys:
raise UnknownKeyName(key)
while 1:
ret = read_db(self.db, key)
if not block or ret is not None:
break
# is polling really the best we can do?
time.sleep(0.05)
return ret
def put(self, key, dat):
if key not in keys:
raise UnknownKeyName(key)
write_db(self.db, key, dat)
from openpilot.common.params_pyx import Params, ParamKeyType, UnknownKeyName
assert Params
assert ParamKeyType
assert UnknownKeyName
if __name__ == "__main__":
params = Params()
if len(sys.argv) > 2:
params.put(sys.argv[1], sys.argv[2])
else:
for k in keys:
pp = params.get(k)
if pp is None:
print("%s is None" % k)
elif all(ord(c) < 128 and ord(c) >= 32 for c in pp):
print("%s = %s" % (k, pp))
else:
print("%s = %s" % (k, pp.encode("hex")))
import sys
# Test multiprocess:
# seq 0 100000 | xargs -P20 -I{} python common/params.py DongleId {} && sleep 0.05
# while python common/params.py DongleId; do sleep 0.05; done
params = Params()
key = sys.argv[1]
assert params.check_key(key), f"unknown param: {key}"
if len(sys.argv) == 3:
val = sys.argv[2]
print(f"SET: {key} = {val}")
params.put(key, val)
elif len(sys.argv) == 2:
print(f"GET: {key} = {params.get(key)}")
+19915
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+163
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@@ -0,0 +1,163 @@
# distutils: language = c++
# cython: language_level = 3
from libcpp cimport bool
from libcpp.string cimport string
from libcpp.vector cimport vector
cdef extern from "common/params.h":
cpdef enum ParamKeyType:
PERSISTENT
CLEAR_ON_MANAGER_START
CLEAR_ON_ONROAD_TRANSITION
CLEAR_ON_OFFROAD_TRANSITION
DEVELOPMENT_ONLY
ALL
cdef cppclass c_Params "Params":
c_Params(string) except + nogil
string get(string, bool) nogil
bool getBool(string, bool) nogil
int getInt(string, bool) nogil
float getFloat(string, bool) nogil
int remove(string) nogil
int put(string, string) nogil
void putNonBlocking(string, string) nogil
void putBoolNonBlocking(string, bool) nogil
void putIntNonBlocking(string, int) nogil
void putFloatNonBlocking(string, float) nogil
int putBool(string, bool) nogil
int putInt(string, int) nogil
int putFloat(string, float) nogil
bool checkKey(string) nogil
string getParamPath(string) nogil
void clearAll(ParamKeyType)
vector[string] allKeys()
ParamKeyType getKeyType(string) nogil
def ensure_bytes(v):
return v.encode() if isinstance(v, str) else v
class UnknownKeyName(Exception):
pass
cdef class Params:
cdef c_Params* p
def __cinit__(self, d=""):
cdef string path = <string>d.encode()
with nogil:
self.p = new c_Params(path)
def __dealloc__(self):
del self.p
def clear_all(self, tx_type=ParamKeyType.ALL):
self.p.clearAll(tx_type)
def check_key(self, key):
key = ensure_bytes(key)
if not self.p.checkKey(key):
raise UnknownKeyName(key)
return key
def get(self, key, bool block=False, encoding=None):
cdef string k = self.check_key(key)
cdef string val
with nogil:
val = self.p.get(k, block)
if val == b"":
if block:
# If we got no value while running in blocked mode
# it means we got an interrupt while waiting
raise KeyboardInterrupt
else:
return None
return val if encoding is None else val.decode(encoding)
def get_bool(self, key, bool block=False):
cdef string k = self.check_key(key)
cdef bool r
with nogil:
r = self.p.getBool(k, block)
return r
def get_int(self, key, bool block=False):
cdef string k = self.check_key(key)
cdef int r
with nogil:
r = self.p.getInt(k, block)
return r
def get_float(self, key, bool block=False):
cdef string k = self.check_key(key)
cdef float r
with nogil:
r = self.p.getFloat(k, block)
return r
def put(self, key, dat):
"""
Warning: This function blocks until the param is written to disk!
In very rare cases this can take over a second, and your code will hang.
Use the put_nonblocking, put_bool_nonblocking in time sensitive code, but
in general try to avoid writing params as much as possible.
"""
cdef string k = self.check_key(key)
cdef string dat_bytes = ensure_bytes(dat)
with nogil:
self.p.put(k, dat_bytes)
def put_bool(self, key, bool val):
cdef string k = self.check_key(key)
with nogil:
self.p.putBool(k, val)
def put_int(self, key, int val):
cdef string k = self.check_key(key)
with nogil:
self.p.putInt(k, val)
def put_float(self, key, float val):
cdef string k = self.check_key(key)
with nogil:
self.p.putFloat(k, val)
def put_nonblocking(self, key, dat):
cdef string k = self.check_key(key)
cdef string dat_bytes = ensure_bytes(dat)
with nogil:
self.p.putNonBlocking(k, dat_bytes)
def put_bool_nonblocking(self, key, bool val):
cdef string k = self.check_key(key)
with nogil:
self.p.putBoolNonBlocking(k, val)
def put_int_nonblocking(self, key, int val):
cdef string k = self.check_key(key)
with nogil:
self.p.putIntNonBlocking(k, val)
def put_float_nonblocking(self, key, float val):
cdef string k = self.check_key(key)
with nogil:
self.p.putFloatNonBlocking(k, val)
def remove(self, key):
cdef string k = self.check_key(key)
with nogil:
self.p.remove(k)
def get_param_path(self, key=""):
cdef string key_bytes = ensure_bytes(key)
return self.p.getParamPath(key_bytes).decode("utf-8")
def all_keys(self):
return self.p.allKeys()
def get_key_type(self, key):
cdef string k = self.check_key(key)
return self.p.getKeyType(k)
BIN
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+53
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@@ -0,0 +1,53 @@
import os
import shutil
import uuid
from openpilot.common.params import Params
from openpilot.system.hardware import PC
from openpilot.system.hardware.hw import Paths
from openpilot.system.hardware.hw import DEFAULT_DOWNLOAD_CACHE_ROOT
class OpenpilotPrefix:
def __init__(self, prefix: str = None, clean_dirs_on_exit: bool = True, shared_download_cache: bool = False):
self.prefix = prefix if prefix else str(uuid.uuid4().hex[0:15])
self.msgq_path = os.path.join('/dev/shm', self.prefix)
self.clean_dirs_on_exit = clean_dirs_on_exit
self.shared_download_cache = shared_download_cache
def __enter__(self):
self.original_prefix = os.environ.get('OPENPILOT_PREFIX', None)
os.environ['OPENPILOT_PREFIX'] = self.prefix
try:
os.mkdir(self.msgq_path)
except FileExistsError:
pass
os.makedirs(Paths.log_root(), exist_ok=True)
if self.shared_download_cache:
os.environ["COMMA_CACHE"] = DEFAULT_DOWNLOAD_CACHE_ROOT
return self
def __exit__(self, exc_type, exc_obj, exc_tb):
if self.clean_dirs_on_exit:
self.clean_dirs()
try:
del os.environ['OPENPILOT_PREFIX']
if self.original_prefix is not None:
os.environ['OPENPILOT_PREFIX'] = self.original_prefix
except KeyError:
pass
return False
def clean_dirs(self):
symlink_path = Params().get_param_path()
if os.path.exists(symlink_path):
shutil.rmtree(os.path.realpath(symlink_path), ignore_errors=True)
os.remove(symlink_path)
shutil.rmtree(self.msgq_path, ignore_errors=True)
if PC:
shutil.rmtree(Paths.log_root(), ignore_errors=True)
if not os.environ.get("COMMA_CACHE", False):
shutil.rmtree(Paths.download_cache_root(), ignore_errors=True)
shutil.rmtree(Paths.comma_home(), ignore_errors=True)
-46
View File
@@ -1,46 +0,0 @@
import time
class Profiler(object):
def __init__(self, enabled=False):
self.enabled = enabled
self.cp = {}
self.cp_ignored = []
self.iter = 0
self.start_time = time.time()
self.last_time = self.start_time
self.tot = 0.
def reset(self, enabled=False):
self.enabled = enabled
self.cp = {}
self.cp_ignored = []
self.iter = 0
self.start_time = time.time()
self.last_time = self.start_time
def checkpoint(self, name, ignore=False):
# ignore flag needed when benchmarking threads with ratekeeper
if not self.enabled:
return
tt = time.time()
if name not in self.cp:
self.cp[name] = 0.
if ignore:
self.cp_ignored.append(name)
self.cp[name] += tt - self.last_time
if not ignore:
self.tot += tt - self.last_time
self.last_time = tt
def display(self):
if not self.enabled:
return
self.iter += 1
print("******* Profiling *******")
for n, ms in sorted(self.cp.items(), key=lambda x: -x[1]):
if n in self.cp_ignored:
print("%30s: %7.2f percent: %3.0f IGNORED" % (n, ms*1000.0, ms/self.tot*100))
else:
print("%30s: %7.2f percent: %3.0f" % (n, ms*1000.0, ms/self.tot*100))
print("Iter clock: %2.6f TOTAL: %2.2f" % (self.tot/self.iter, self.tot))
+55 -42
View File
@@ -1,80 +1,93 @@
"""Utilities for reading real time clocks and keeping soft real time constraints."""
import gc
import os
import time
import platform
import subprocess
import multiprocessing
from cffi import FFI
from collections import deque
# Build and load cython module
import pyximport
installer = pyximport.install(inplace=True, build_dir='/tmp')
from common.clock import monotonic_time, sec_since_boot # pylint: disable=no-name-in-module, import-error
pyximport.uninstall(*installer)
assert monotonic_time
assert sec_since_boot
from setproctitle import getproctitle
from openpilot.system.hardware import PC
# time step for each process
DT_CTRL = 0.01 # controlsd
DT_PLAN = 0.05 # mpc
DT_MDL = 0.05 # model
DT_DMON = 0.1 # driver monitoring
DT_TRML = 0.5 # thermald and manager
DT_HW = 0.5 # hardwared and manager
DT_DMON = 0.05 # driver monitoring
ffi = FFI()
ffi.cdef("long syscall(long number, ...);")
libc = ffi.dlopen(None)
class Priority:
# CORE 2
# - modeld = 55
# - camerad = 54
CTRL_LOW = 51 # plannerd & radard
# CORE 3
# - pandad = 55
CTRL_HIGH = 53
def set_realtime_priority(level):
if os.getuid() != 0:
print("not setting priority, not root")
return
if platform.machine() == "x86_64":
NR_gettid = 186
elif platform.machine() == "aarch64":
NR_gettid = 178
else:
raise NotImplementedError
tid = libc.syscall(NR_gettid)
return subprocess.call(['chrt', '-f', '-p', str(level), str(tid)])
def set_realtime_priority(level: int) -> None:
if not PC:
os.sched_setscheduler(0, os.SCHED_FIFO, os.sched_param(level))
class Ratekeeper(object):
def __init__(self, rate, print_delay_threshold=0.):
def set_core_affinity(cores: list[int]) -> None:
if not PC:
os.sched_setaffinity(0, cores)
def config_realtime_process(cores: int | list[int], priority: int) -> None:
gc.disable()
set_realtime_priority(priority)
c = cores if isinstance(cores, list) else [cores, ]
set_core_affinity(c)
class Ratekeeper:
def __init__(self, rate: float, print_delay_threshold: float | None = 0.0) -> None:
"""Rate in Hz for ratekeeping. print_delay_threshold must be nonnegative."""
self._interval = 1. / rate
self._next_frame_time = sec_since_boot() + self._interval
self._next_frame_time = time.monotonic() + self._interval
self._print_delay_threshold = print_delay_threshold
self._frame = 0
self._remaining = 0
self._process_name = multiprocessing.current_process().name
self._remaining = 0.0
self._process_name = getproctitle()
self._dts = deque([self._interval], maxlen=100)
self._last_monitor_time = time.monotonic()
@property
def frame(self):
def frame(self) -> int:
return self._frame
@property
def remaining(self):
def remaining(self) -> float:
return self._remaining
@property
def lagging(self) -> bool:
avg_dt = sum(self._dts) / len(self._dts)
expected_dt = self._interval * (1 / 0.9)
return avg_dt > expected_dt
# Maintain loop rate by calling this at the end of each loop
def keep_time(self):
def keep_time(self) -> bool:
lagged = self.monitor_time()
if self._remaining > 0:
time.sleep(self._remaining)
return lagged
# this only monitor the cumulative lag, but does not enforce a rate
def monitor_time(self):
# Monitors the cumulative lag, but does not enforce a rate
def monitor_time(self) -> bool:
prev = self._last_monitor_time
self._last_monitor_time = time.monotonic()
self._dts.append(self._last_monitor_time - prev)
lagged = False
remaining = self._next_frame_time - sec_since_boot()
remaining = self._next_frame_time - time.monotonic()
self._next_frame_time += self._interval
if self._print_delay_threshold is not None and remaining < -self._print_delay_threshold:
print("%s lagging by %.2f ms" % (self._process_name, -remaining * 1000))
print(f"{self._process_name} lagging by {-remaining * 1000:.2f} ms")
lagged = True
self._frame += 1
self._remaining = remaining
+30
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@@ -0,0 +1,30 @@
import time
import functools
from openpilot.common.swaglog import cloudlog
def retry(attempts=3, delay=1.0, ignore_failure=False):
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
for _ in range(attempts):
try:
return func(*args, **kwargs)
except Exception:
cloudlog.exception(f"{func.__name__} failed, trying again")
time.sleep(delay)
if ignore_failure:
cloudlog.error(f"{func.__name__} failed after retry")
else:
raise Exception(f"{func.__name__} failed after retry")
return wrapper
return decorator
if __name__ == "__main__":
@retry(attempts=10)
def abc():
raise ValueError("abc failed :(")
abc()
+13
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@@ -0,0 +1,13 @@
import subprocess
def run_cmd(cmd: list[str], cwd=None, env=None) -> str:
return subprocess.check_output(cmd, encoding='utf8', cwd=cwd, env=env).strip()
def run_cmd_default(cmd: list[str], default: str = "", cwd=None, env=None) -> str:
try:
return run_cmd(cmd, cwd=cwd, env=env)
except subprocess.CalledProcessError:
return default
+54
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@@ -0,0 +1,54 @@
import numpy as np
def get_kalman_gain(dt, A, C, Q, R, iterations=100):
P = np.zeros_like(Q)
for _ in range(iterations):
P = A.dot(P).dot(A.T) + dt * Q
S = C.dot(P).dot(C.T) + R
K = P.dot(C.T).dot(np.linalg.inv(S))
P = (np.eye(len(P)) - K.dot(C)).dot(P)
return K
class KF1D:
# this EKF assumes constant covariance matrix, so calculations are much simpler
# the Kalman gain also needs to be precomputed using the control module
def __init__(self, x0, A, C, K):
self.x0_0 = x0[0][0]
self.x1_0 = x0[1][0]
self.A0_0 = A[0][0]
self.A0_1 = A[0][1]
self.A1_0 = A[1][0]
self.A1_1 = A[1][1]
self.C0_0 = C[0]
self.C0_1 = C[1]
self.K0_0 = K[0][0]
self.K1_0 = K[1][0]
self.A_K_0 = self.A0_0 - self.K0_0 * self.C0_0
self.A_K_1 = self.A0_1 - self.K0_0 * self.C0_1
self.A_K_2 = self.A1_0 - self.K1_0 * self.C0_0
self.A_K_3 = self.A1_1 - self.K1_0 * self.C0_1
# K matrix needs to be pre-computed as follow:
# import control
# (x, l, K) = control.dare(np.transpose(self.A), np.transpose(self.C), Q, R)
# self.K = np.transpose(K)
def update(self, meas):
#self.x = np.dot(self.A_K, self.x) + np.dot(self.K, meas)
x0_0 = self.A_K_0 * self.x0_0 + self.A_K_1 * self.x1_0 + self.K0_0 * meas
x1_0 = self.A_K_2 * self.x0_0 + self.A_K_3 * self.x1_0 + self.K1_0 * meas
self.x0_0 = x0_0
self.x1_0 = x1_0
return [self.x0_0, self.x1_0]
@property
def x(self):
return [[self.x0_0], [self.x1_0]]
def set_x(self, x):
self.x0_0 = x[0][0]
self.x1_0 = x[1][0]
+52
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@@ -0,0 +1,52 @@
import os
import subprocess
from openpilot.common.basedir import BASEDIR
class Spinner:
def __init__(self):
try:
self.spinner_proc = subprocess.Popen(["./spinner"],
stdin=subprocess.PIPE,
cwd=os.path.join(BASEDIR, "selfdrive", "ui"),
close_fds=True)
except OSError:
self.spinner_proc = None
def __enter__(self):
return self
def update(self, spinner_text: str):
if self.spinner_proc is not None:
self.spinner_proc.stdin.write(spinner_text.encode('utf8') + b"\n")
try:
self.spinner_proc.stdin.flush()
except BrokenPipeError:
pass
def update_progress(self, cur: float, total: float):
self.update(str(round(100 * cur / total)))
def close(self):
if self.spinner_proc is not None:
self.spinner_proc.kill()
try:
self.spinner_proc.communicate(timeout=2.)
except subprocess.TimeoutExpired:
print("WARNING: failed to kill spinner")
self.spinner_proc = None
def __del__(self):
self.close()
def __exit__(self, exc_type, exc_value, traceback):
self.close()
if __name__ == "__main__":
import time
with Spinner() as s:
s.update("Spinner text")
time.sleep(5.0)
print("gone")
time.sleep(5.0)
+73
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@@ -0,0 +1,73 @@
import numpy as np
class RunningStat:
# tracks realtime mean and standard deviation without storing any data
def __init__(self, priors=None, max_trackable=-1):
self.max_trackable = max_trackable
if priors is not None:
# initialize from history
self.M = priors[0]
self.S = priors[1]
self.n = priors[2]
self.M_last = self.M
self.S_last = self.S
else:
self.reset()
def reset(self):
self.M = 0.
self.S = 0.
self.M_last = 0.
self.S_last = 0.
self.n = 0
def push_data(self, new_data):
# short term memory hack
if self.max_trackable < 0 or self.n < self.max_trackable:
self.n += 1
if self.n == 0:
self.M_last = new_data
self.M = self.M_last
self.S_last = 0.
else:
self.M = self.M_last + (new_data - self.M_last) / self.n
self.S = self.S_last + (new_data - self.M_last) * (new_data - self.M)
self.M_last = self.M
self.S_last = self.S
def mean(self):
return self.M
def variance(self):
if self.n >= 2:
return self.S / (self.n - 1.)
else:
return 0
def std(self):
return np.sqrt(self.variance())
def params_to_save(self):
return [self.M, self.S, self.n]
class RunningStatFilter:
def __init__(self, raw_priors=None, filtered_priors=None, max_trackable=-1):
self.raw_stat = RunningStat(raw_priors, -1)
self.filtered_stat = RunningStat(filtered_priors, max_trackable)
def reset(self):
self.raw_stat.reset()
self.filtered_stat.reset()
def push_and_update(self, new_data):
_std_last = self.raw_stat.std()
self.raw_stat.push_data(new_data)
_delta_std = self.raw_stat.std() - _std_last
if _delta_std <= 0:
self.filtered_stat.push_data(new_data)
else:
pass
# self.filtered_stat.push_data(self.filtered_stat.mean())
# class SequentialBayesian():
+135
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@@ -0,0 +1,135 @@
import logging
import os
import time
import warnings
from pathlib import Path
from logging.handlers import BaseRotatingHandler
import zmq
from openpilot.common.logging_extra import SwagLogger, SwagFormatter, SwagLogFileFormatter
from openpilot.system.hardware.hw import Paths
def get_file_handler():
Path(Paths.swaglog_root()).mkdir(parents=True, exist_ok=True)
base_filename = os.path.join(Paths.swaglog_root(), "swaglog")
handler = SwaglogRotatingFileHandler(base_filename)
return handler
class SwaglogRotatingFileHandler(BaseRotatingHandler):
def __init__(self, base_filename, interval=60, max_bytes=1024*256, backup_count=2500, encoding=None):
super().__init__(base_filename, mode="a", encoding=encoding, delay=True)
self.base_filename = base_filename
self.interval = interval # seconds
self.max_bytes = max_bytes
self.backup_count = backup_count
self.log_files = self.get_existing_logfiles()
log_indexes = [f.split(".")[-1] for f in self.log_files]
self.last_file_idx = max([int(i) for i in log_indexes if i.isdigit()] or [-1])
self.last_rollover = None
self.doRollover()
def _open(self):
self.last_rollover = time.monotonic()
self.last_file_idx += 1
next_filename = f"{self.base_filename}.{self.last_file_idx:010}"
stream = open(next_filename, self.mode, encoding=self.encoding)
self.log_files.insert(0, next_filename)
return stream
def get_existing_logfiles(self):
log_files = list()
base_dir = os.path.dirname(self.base_filename)
for fn in os.listdir(base_dir):
fp = os.path.join(base_dir, fn)
if fp.startswith(self.base_filename) and os.path.isfile(fp):
log_files.append(fp)
return sorted(log_files)
def shouldRollover(self, record):
size_exceeded = self.max_bytes > 0 and self.stream.tell() >= self.max_bytes
time_exceeded = self.interval > 0 and self.last_rollover + self.interval <= time.monotonic()
return size_exceeded or time_exceeded
def doRollover(self):
if self.stream:
self.stream.close()
self.stream = self._open()
if self.backup_count > 0:
while len(self.log_files) > self.backup_count:
to_delete = self.log_files.pop()
if os.path.exists(to_delete): # just being safe, should always exist
os.remove(to_delete)
class UnixDomainSocketHandler(logging.Handler):
def __init__(self, formatter):
logging.Handler.__init__(self)
self.setFormatter(formatter)
self.pid = None
self.zctx = None
self.sock = None
def __del__(self):
self.close()
def close(self):
if self.sock is not None:
self.sock.close()
if self.zctx is not None:
self.zctx.term()
def connect(self):
self.zctx = zmq.Context()
self.sock = self.zctx.socket(zmq.PUSH)
self.sock.setsockopt(zmq.LINGER, 10)
self.sock.connect(Paths.swaglog_ipc())
self.pid = os.getpid()
def emit(self, record):
if os.getpid() != self.pid:
# TODO suppresses warning about forking proc with zmq socket, fix root cause
warnings.filterwarnings("ignore", category=ResourceWarning, message="unclosed.*<zmq.*>")
self.connect()
msg = self.format(record).rstrip('\n')
# print("SEND".format(repr(msg)))
try:
s = chr(record.levelno)+msg
self.sock.send(s.encode('utf8'), zmq.NOBLOCK)
except zmq.error.Again:
# drop :/
pass
def add_file_handler(log):
"""
Function to add the file log handler to swaglog.
This can be used to store logs when logmessaged is not running.
"""
handler = get_file_handler()
handler.setFormatter(SwagLogFileFormatter(log))
log.addHandler(handler)
cloudlog = log = SwagLogger()
log.setLevel(logging.DEBUG)
outhandler = logging.StreamHandler()
print_level = os.environ.get('LOGPRINT', 'warning')
if print_level == 'debug':
outhandler.setLevel(logging.DEBUG)
elif print_level == 'info':
outhandler.setLevel(logging.INFO)
elif print_level == 'warning':
outhandler.setLevel(logging.WARNING)
ipchandler = UnixDomainSocketHandler(SwagFormatter(log))
log.addHandler(outhandler)
# logs are sent through IPC before writing to disk to prevent disk I/O blocking
log.addHandler(ipchandler)
-81
View File
@@ -1,81 +0,0 @@
#!/usr/bin/env python
import sympy as sp
import numpy as np
def cross(x):
ret = sp.Matrix(np.zeros((3,3)))
ret[0,1], ret[0,2] = -x[2], x[1]
ret[1,0], ret[1,2] = x[2], -x[0]
ret[2,0], ret[2,1] = -x[1], x[0]
return ret
def euler_rotate(roll, pitch, yaw):
# make symbolic rotation matrix from eulers
matrix_roll = sp.Matrix([[1, 0, 0],
[0, sp.cos(roll), -sp.sin(roll)],
[0, sp.sin(roll), sp.cos(roll)]])
matrix_pitch = sp.Matrix([[sp.cos(pitch), 0, sp.sin(pitch)],
[0, 1, 0],
[-sp.sin(pitch), 0, sp.cos(pitch)]])
matrix_yaw = sp.Matrix([[sp.cos(yaw), -sp.sin(yaw), 0],
[sp.sin(yaw), sp.cos(yaw), 0],
[0, 0, 1]])
return matrix_yaw*matrix_pitch*matrix_roll
def quat_rotate(q0, q1, q2, q3):
# make symbolic rotation matrix from quat
return sp.Matrix([[q0**2 + q1**2 - q2**2 - q3**2, 2*(q1*q2 + q0*q3), 2*(q1*q3 - q0*q2)],
[2*(q1*q2 - q0*q3), q0**2 - q1**2 + q2**2 - q3**2, 2*(q2*q3 + q0*q1)],
[2*(q1*q3 + q0*q2), 2*(q2*q3 - q0*q1), q0**2 - q1**2 - q2**2 + q3**2]]).T
def quat_matrix_l(p):
return sp.Matrix([[p[0], -p[1], -p[2], -p[3]],
[p[1], p[0], -p[3], p[2]],
[p[2], p[3], p[0], -p[1]],
[p[3], -p[2], p[1], p[0]]])
def quat_matrix_r(p):
return sp.Matrix([[p[0], -p[1], -p[2], -p[3]],
[p[1], p[0], p[3], -p[2]],
[p[2], -p[3], p[0], p[1]],
[p[3], p[2], -p[1], p[0]]])
def sympy_into_c(sympy_functions):
from sympy.utilities import codegen
routines = []
for name, expr, args in sympy_functions:
r = codegen.make_routine(name, expr, language="C99")
# argument ordering input to sympy is broken with function with output arguments
nargs = []
# reorder the input arguments
for aa in args:
if aa is None:
nargs.append(codegen.InputArgument(sp.Symbol('unused'), dimensions=[1,1]))
continue
found = False
for a in r.arguments:
if str(aa.name) == str(a.name):
nargs.append(a)
found = True
break
if not found:
# [1,1] is a hack for Matrices
nargs.append(codegen.InputArgument(aa, dimensions=[1,1]))
# add the output arguments
for a in r.arguments:
if type(a) == codegen.OutputArgument:
nargs.append(a)
#assert len(r.arguments) == len(args)+1
r.arguments = nargs
# add routine to list
routines.append(r)
[(c_name, c_code), (h_name, c_header)] = codegen.get_code_generator('C', 'ekf', 'C99').write(routines, "ekf")
c_code = '\n'.join(x for x in c_code.split("\n") if len(x) > 0 and x[0] != '#')
c_header = '\n'.join(x for x in c_header.split("\n") if len(x) > 0 and x[0] != '#')
return c_header, c_code
-9
View File
@@ -1,9 +0,0 @@
import os
from nose.tools import nottest
def phone_only(x):
if os.path.isfile("/init.qcom.rc"):
return x
else:
return nottest(x)
+63
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@@ -0,0 +1,63 @@
#!/usr/bin/env python3
import os
import time
import subprocess
from openpilot.common.basedir import BASEDIR
class TextWindow:
def __init__(self, text):
try:
self.text_proc = subprocess.Popen(["./text", text],
stdin=subprocess.PIPE,
cwd=os.path.join(BASEDIR, "selfdrive", "ui"),
close_fds=True)
except OSError:
self.text_proc = None
def get_status(self):
if self.text_proc is not None:
self.text_proc.poll()
return self.text_proc.returncode
return None
def __enter__(self):
return self
def close(self):
if self.text_proc is not None:
self.text_proc.terminate()
self.text_proc = None
def wait_for_exit(self):
if self.text_proc is not None:
while True:
if self.get_status() == 1:
return
time.sleep(0.1)
def __del__(self):
self.close()
def __exit__(self, exc_type, exc_value, traceback):
self.close()
if __name__ == "__main__":
text = """Traceback (most recent call last):
File "./controlsd.py", line 608, in <module>
main()
File "./controlsd.py", line 604, in main
controlsd_thread(sm, pm, logcan)
File "./controlsd.py", line 455, in controlsd_thread
1/0
ZeroDivisionError: division by zero"""
print(text)
with TextWindow(text) as s:
for _ in range(100):
if s.get_status() == 1:
print("Got exit button")
break
time.sleep(0.1)
print("gone")
+15
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@@ -0,0 +1,15 @@
import datetime
from pathlib import Path
_MIN_DATE = datetime.datetime(year=2024, month=3, day=30)
def min_date():
# on systemd systems, the default time is the systemd build time
systemd_path = Path("/lib/systemd/systemd")
if systemd_path.exists():
d = datetime.datetime.fromtimestamp(systemd_path.stat().st_mtime)
return d + datetime.timedelta(days=1)
return _MIN_DATE
def system_time_valid():
return datetime.datetime.now() > min_date()
+1 -2
View File
@@ -12,7 +12,7 @@ class Timeout:
"""
def __init__(self, seconds, error_msg=None):
if error_msg is None:
error_msg = 'Timed out after {} seconds'.format(seconds)
error_msg = f'Timed out after {seconds} seconds'
self.seconds = seconds
self.error_msg = error_msg
@@ -25,4 +25,3 @@ class Timeout:
def __exit__(self, exc_type, exc_val, exc_tb):
signal.alarm(0)
+2
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@@ -0,0 +1,2 @@
transformations
transformations.cpp
+70
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@@ -0,0 +1,70 @@
Reference Frames
------
Many reference frames are used throughout. This
folder contains all helper functions needed to
transform between them. Generally this is done
by generating a rotation matrix and multiplying.
| Name | [x, y, z] | Units | Notes |
| :-------------: |:-------------:| :-----:| :----: |
| Geodetic | [Latitude, Longitude, Altitude] | geodetic coordinates | Sometimes used as [lon, lat, alt], avoid this frame. |
| ECEF | [x, y, z] | meters | We use **ITRF14 (IGS14)**, NOT NAD83. <br> This is the global Mesh3D frame. |
| NED | [North, East, Down] | meters | Relative to earth's surface, useful for visualizing. |
| Device | [Forward, Right, Down] | meters | This is the Mesh3D local frame. <br> Relative to camera, **not imu.** <br> ![img](http://upload.wikimedia.org/wikipedia/commons/thumb/2/2f/RPY_angles_of_airplanes.png/440px-RPY_angles_of_airplanes.png)|
| Calibrated | [Forward, Right, Down] | meters | This is the frame the model outputs are in. <br> More details below. <br>|
| Car | [Forward, Right, Down] | meters | This is useful for estimating position of points on the road. <br> More details below. <br>|
| View | [Right, Down, Forward] | meters | Like device frame, but according to camera conventions. |
| Camera | [u, v, focal] | pixels | Like view frame, but 2d on the camera image.|
| Normalized Camera | [u / focal, v / focal, 1] | / | |
| Model | [u, v, focal] | pixels | The sampled rectangle of the full camera frame the model uses. |
| Normalized Model | [u / focal, v / focal, 1] | / | |
Orientation Conventions
------
Quaternions, rotation matrices and euler angles are three
equivalent representations of orientation and all three are
used throughout the code base.
For euler angles the preferred convention is [roll, pitch, yaw]
which corresponds to rotations around the [x, y, z] axes. All
euler angles should always be in radians or radians/s unless
for plotting or display purposes. For quaternions the hamilton
notations is preferred which is [q<sub>w</sub>, q<sub>x</sub>, q<sub>y</sub>, q<sub>z</sub>]. All quaternions
should always be normalized with a strictly positive q<sub>w</sub>. **These
quaternions are a unique representation of orientation whereas euler angles
or rotation matrices are not.**
To rotate from one frame into another with euler angles the
convention is to rotate around roll, then pitch and then yaw,
while rotating around the rotated axes, not the original axes.
Car frame
------
Device frame is aligned with the road-facing camera used by openpilot. However, when controlling the vehicle it is helpful to think in a reference frame aligned with the vehicle. These two reference frames can be different.
The orientation of car frame is defined to be aligned with the car's direction of travel and the road plane when the vehicle is driving on a flat road and not turning. The origin of car frame is defined to be directly below device frame (in car frame), such that it is on the road plane. The position and orientation of this frame is not necessarily always aligned with the direction of travel or the road plane due to suspension movements and other effects.
Calibrated frame
------
It is helpful for openpilot's driving model to take in images that look similar when mounted differently in different cars. To achieve this we "calibrate" the images by transforming it into calibrated frame. Calibrated frame is defined to be aligned with car frame in pitch and yaw, and aligned with device frame in roll. It also has the same origin as device frame.
Example
------
To transform global Mesh3D positions and orientations (positions_ecef, quats_ecef) into the local frame described by the
first position and orientation from Mesh3D one would do:
```
ecef_from_local = rot_from_quat(quats_ecef[0])
local_from_ecef = ecef_from_local.T
positions_local = np.einsum('ij,kj->ki', local_from_ecef, postions_ecef - positions_ecef[0])
rotations_global = rot_from_quat(quats_ecef)
rotations_local = np.einsum('ij,kjl->kil', local_from_ecef, rotations_global)
eulers_local = euler_from_rot(rotations_local)
```
+96 -133
View File
@@ -1,31 +1,74 @@
import itertools
import numpy as np
import common.transformations.orientation as orient
import math
from dataclasses import dataclass
FULL_FRAME_SIZE = (1164, 874)
W, H = FULL_FRAME_SIZE[0], FULL_FRAME_SIZE[1]
eon_focal_length = FOCAL = 910.0
import openpilot.common.transformations.orientation as orient
# aka 'K' aka camera_frame_from_view_frame
eon_intrinsics = np.array([
[FOCAL, 0., W/2.],
[ 0., FOCAL, H/2.],
[ 0., 0., 1.]])
## -- hardcoded hardware params --
@dataclass(frozen=True)
class CameraConfig:
width: int
height: int
focal_length: float
@property
def size(self):
return (self.width, self.height)
leon_dcam_intrinsics = np.array([
[650, 0, 816//2],
[ 0, 650, 612//2],
[ 0, 0, 1]])
@property
def intrinsics(self):
# aka 'K' aka camera_frame_from_view_frame
return np.array([
[self.focal_length, 0.0, float(self.width)/2],
[0.0, self.focal_length, float(self.height)/2],
[0.0, 0.0, 1.0]
])
eon_dcam_intrinsics = np.array([
[860, 0, 1152//2],
[ 0, 860, 864//2],
[ 0, 0, 1]])
@property
def intrinsics_inv(self):
# aka 'K_inv' aka view_frame_from_camera_frame
return np.linalg.inv(self.intrinsics)
# aka 'K_inv' aka view_frame_from_camera_frame
eon_intrinsics_inv = np.linalg.inv(eon_intrinsics)
@dataclass(frozen=True)
class _NoneCameraConfig(CameraConfig):
width: int = 0
height: int = 0
focal_length: float = 0
@dataclass(frozen=True)
class DeviceCameraConfig:
fcam: CameraConfig
dcam: CameraConfig
ecam: CameraConfig
def all_cams(self):
for cam in ['fcam', 'dcam', 'ecam']:
if not isinstance(getattr(self, cam), _NoneCameraConfig):
yield cam, getattr(self, cam)
_ar_ox_fisheye = CameraConfig(1928, 1208, 567.0) # focal length probably wrong? magnification is not consistent across frame
_os_fisheye = CameraConfig(2688, 1520, 567.0 / 2 * 3)
_ar_ox_config = DeviceCameraConfig(CameraConfig(1928, 1208, 2648.0), _ar_ox_fisheye, _ar_ox_fisheye)
_os_config = DeviceCameraConfig(CameraConfig(2688, 1520, 2648.0 * 2 / 3), _os_fisheye, _os_fisheye)
_neo_config = DeviceCameraConfig(CameraConfig(1164, 874, 910.0), CameraConfig(816, 612, 650.0), _NoneCameraConfig())
DEVICE_CAMERAS = {
# A "device camera" is defined by a device type and sensor
# sensor type was never set on eon/neo/two
("neo", "unknown"): _neo_config,
# unknown here is AR0231, field was added with OX03C10 support
("tici", "unknown"): _ar_ox_config,
# before deviceState.deviceType was set, assume tici AR config
("unknown", "ar0231"): _ar_ox_config,
("unknown", "ox03c10"): _ar_ox_config,
# simulator (emulates a tici)
("pc", "unknown"): _ar_ox_config,
}
prods = itertools.product(('tici', 'tizi', 'mici'), (('ar0231', _ar_ox_config), ('ox03c10', _ar_ox_config), ('os04c10', _os_config)))
DEVICE_CAMERAS.update({(d, c[0]): c[1] for d, c in prods})
# device/mesh : x->forward, y-> right, z->down
# view : x->right, y->down, z->forward
@@ -37,13 +80,6 @@ device_frame_from_view_frame = np.array([
view_frame_from_device_frame = device_frame_from_view_frame.T
def get_calib_from_vp(vp):
vp_norm = normalize(vp)
yaw_calib = np.arctan(vp_norm[0])
pitch_calib = -np.arctan(vp_norm[1]*np.cos(yaw_calib))
roll_calib = 0
return roll_calib, pitch_calib, yaw_calib
# aka 'extrinsic_matrix'
# road : x->forward, y -> left, z->up
def get_view_frame_from_road_frame(roll, pitch, yaw, height):
@@ -52,6 +88,14 @@ def get_view_frame_from_road_frame(roll, pitch, yaw, height):
return np.hstack((view_from_road, [[0], [height], [0]]))
# aka 'extrinsic_matrix'
def get_view_frame_from_calib_frame(roll, pitch, yaw, height):
device_from_calib= orient.rot_from_euler([roll, pitch, yaw])
view_from_calib = view_frame_from_device_frame.dot(device_from_calib)
return np.hstack((view_from_calib, [[0], [height], [0]]))
def vp_from_ke(m):
"""
Computes the vanishing point from the product of the intrinsic and extrinsic
@@ -59,7 +103,8 @@ def vp_from_ke(m):
The vanishing point is defined as lim x->infinity C (x, 0, 0, 1).T
"""
return (m[0, 0]/m[2,0], m[1,0]/m[2,0])
return (m[0, 0]/m[2, 0], m[1, 0]/m[2, 0])
def roll_from_ke(m):
# note: different from calibration.h/RollAnglefromKE: i think that one's just wrong
@@ -67,32 +112,42 @@ def roll_from_ke(m):
-(m[0, 0] - m[0, 1] * m[2, 0] / m[2, 1]))
def normalize(img_pts, intrinsics=eon_intrinsics):
def normalize(img_pts, intrinsics):
# normalizes image coordinates
# accepts single pt or array of pts
intrinsics_inv = np.linalg.inv(intrinsics)
img_pts = np.array(img_pts)
input_shape = img_pts.shape
img_pts = np.atleast_2d(img_pts)
img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0],1))))
img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0], 1))))
img_pts_normalized = img_pts.dot(intrinsics_inv.T)
img_pts_normalized[(img_pts < 0).any(axis=1)] = np.nan
return img_pts_normalized[:,:2].reshape(input_shape)
return img_pts_normalized[:, :2].reshape(input_shape)
def denormalize(img_pts, intrinsics=eon_intrinsics):
def denormalize(img_pts, intrinsics, width=np.inf, height=np.inf):
# denormalizes image coordinates
# accepts single pt or array of pts
img_pts = np.array(img_pts)
input_shape = img_pts.shape
img_pts = np.atleast_2d(img_pts)
img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0],1))))
img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0], 1), dtype=img_pts.dtype)))
img_pts_denormalized = img_pts.dot(intrinsics.T)
img_pts_denormalized[img_pts_denormalized[:,0] > W] = np.nan
img_pts_denormalized[img_pts_denormalized[:,0] < 0] = np.nan
img_pts_denormalized[img_pts_denormalized[:,1] > H] = np.nan
img_pts_denormalized[img_pts_denormalized[:,1] < 0] = np.nan
return img_pts_denormalized[:,:2].reshape(input_shape)
if np.isfinite(width):
img_pts_denormalized[img_pts_denormalized[:, 0] > width] = np.nan
img_pts_denormalized[img_pts_denormalized[:, 0] < 0] = np.nan
if np.isfinite(height):
img_pts_denormalized[img_pts_denormalized[:, 1] > height] = np.nan
img_pts_denormalized[img_pts_denormalized[:, 1] < 0] = np.nan
return img_pts_denormalized[:, :2].reshape(input_shape)
def get_calib_from_vp(vp, intrinsics):
vp_norm = normalize(vp, intrinsics)
yaw_calib = np.arctan(vp_norm[0])
pitch_calib = -np.arctan(vp_norm[1]*np.cos(yaw_calib))
roll_calib = 0
return roll_calib, pitch_calib, yaw_calib
def device_from_ecef(pos_ecef, orientation_ecef, pt_ecef):
@@ -117,100 +172,8 @@ def img_from_device(pt_device):
pt_view = np.einsum('jk,ik->ij', view_frame_from_device_frame, pt_device)
# This function should never return negative depths
pt_view[pt_view[:,2] < 0] = np.nan
pt_view[pt_view[:, 2] < 0] = np.nan
pt_img = pt_view/pt_view[:,2:3]
return pt_img.reshape(input_shape)[:,:2]
pt_img = pt_view/pt_view[:, 2:3]
return pt_img.reshape(input_shape)[:, :2]
#TODO please use generic img transform below
def rotate_img(img, eulers, crop=None, intrinsics=eon_intrinsics):
import cv2
size = img.shape[:2]
rot = orient.rot_from_euler(eulers)
quadrangle = np.array([[0, 0],
[size[1]-1, 0],
[0, size[0]-1],
[size[1]-1, size[0]-1]], dtype=np.float32)
quadrangle_norm = np.hstack((normalize(quadrangle, intrinsics=intrinsics), np.ones((4,1))))
warped_quadrangle_full = np.einsum('ij, kj->ki', intrinsics.dot(rot), quadrangle_norm)
warped_quadrangle = np.column_stack((warped_quadrangle_full[:,0]/warped_quadrangle_full[:,2],
warped_quadrangle_full[:,1]/warped_quadrangle_full[:,2])).astype(np.float32)
if crop:
W_border = (size[1] - crop[0])/2
H_border = (size[0] - crop[1])/2
outside_crop = (((warped_quadrangle[:,0] < W_border) |
(warped_quadrangle[:,0] >= size[1] - W_border)) &
((warped_quadrangle[:,1] < H_border) |
(warped_quadrangle[:,1] >= size[0] - H_border)))
if not outside_crop.all():
raise ValueError("warped image not contained inside crop")
else:
H_border, W_border = 0, 0
M = cv2.getPerspectiveTransform(quadrangle, warped_quadrangle)
img_warped = cv2.warpPerspective(img, M, size[::-1])
return img_warped[H_border: size[0] - H_border,
W_border: size[1] - W_border]
def transform_img(base_img,
augment_trans=np.array([0,0,0]),
augment_eulers=np.array([0,0,0]),
from_intr=eon_intrinsics,
to_intr=eon_intrinsics,
calib_rot_view=None,
output_size=None,
pretransform=None,
top_hacks=True):
import cv2
size = base_img.shape[:2]
if not output_size:
output_size = size[::-1]
cy = from_intr[1,2]
def get_M(h=1.22):
quadrangle = np.array([[0, cy + 20],
[size[1]-1, cy + 20],
[0, size[0]-1],
[size[1]-1, size[0]-1]], dtype=np.float32)
quadrangle_norm = np.hstack((normalize(quadrangle, intrinsics=from_intr), np.ones((4,1))))
quadrangle_world = np.column_stack((h*quadrangle_norm[:,0]/quadrangle_norm[:,1],
h*np.ones(4),
h/quadrangle_norm[:,1]))
rot = orient.rot_from_euler(augment_eulers)
if calib_rot_view is not None:
rot = calib_rot_view.dot(rot)
to_extrinsics = np.hstack((rot.T, -augment_trans[:,None]))
to_KE = to_intr.dot(to_extrinsics)
warped_quadrangle_full = np.einsum('jk,ik->ij', to_KE, np.hstack((quadrangle_world, np.ones((4,1)))))
warped_quadrangle = np.column_stack((warped_quadrangle_full[:,0]/warped_quadrangle_full[:,2],
warped_quadrangle_full[:,1]/warped_quadrangle_full[:,2])).astype(np.float32)
M = cv2.getPerspectiveTransform(quadrangle, warped_quadrangle.astype(np.float32))
return M
M = get_M()
if pretransform is not None:
M = M.dot(pretransform)
augmented_rgb = cv2.warpPerspective(base_img, M, output_size, borderMode=cv2.BORDER_REPLICATE)
if top_hacks:
cyy = int(math.ceil(to_intr[1,2]))
M = get_M(1000)
if pretransform is not None:
M = M.dot(pretransform)
augmented_rgb[:cyy] = cv2.warpPerspective(base_img, M, (output_size[0], cyy), borderMode=cv2.BORDER_REPLICATE)
return augmented_rgb
def yuv_crop(frame, output_size, center=None):
# output_size in camera coordinates so u,v
# center in array coordinates so row, column
import cv2
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
if not center:
center = (rgb.shape[0]/2, rgb.shape[1]/2)
rgb_crop = rgb[center[0] - output_size[1]/2: center[0] + output_size[1]/2,
center[1] - output_size[0]/2: center[1] + output_size[0]/2]
return cv2.cvtColor(rgb_crop, cv2.COLOR_RGB2YUV_I420)
+43
View File
@@ -0,0 +1,43 @@
#pragma once
#include <eigen3/Eigen/Dense>
#define DEG2RAD(x) ((x) * M_PI / 180.0)
#define RAD2DEG(x) ((x) * 180.0 / M_PI)
struct ECEF {
double x, y, z;
Eigen::Vector3d to_vector(){
return Eigen::Vector3d(x, y, z);
}
};
struct NED {
double n, e, d;
Eigen::Vector3d to_vector(){
return Eigen::Vector3d(n, e, d);
}
};
struct Geodetic {
double lat, lon, alt;
bool radians=false;
};
ECEF geodetic2ecef(Geodetic g);
Geodetic ecef2geodetic(ECEF e);
class LocalCoord {
public:
Eigen::Matrix3d ned2ecef_matrix;
Eigen::Matrix3d ecef2ned_matrix;
Eigen::Vector3d init_ecef;
LocalCoord(Geodetic g, ECEF e);
LocalCoord(Geodetic g) : LocalCoord(g, ::geodetic2ecef(g)) {}
LocalCoord(ECEF e) : LocalCoord(::ecef2geodetic(e), e) {}
NED ecef2ned(ECEF e);
ECEF ned2ecef(NED n);
NED geodetic2ned(Geodetic g);
Geodetic ned2geodetic(NED n);
};
+13 -103
View File
@@ -1,108 +1,18 @@
import numpy as np
"""
Coordinate transformation module. All methods accept arrays as input
with each row as a position.
"""
from openpilot.common.transformations.orientation import numpy_wrap
from openpilot.common.transformations.transformations import (ecef2geodetic_single,
geodetic2ecef_single)
from openpilot.common.transformations.transformations import LocalCoord as LocalCoord_single
a = 6378137
b = 6356752.3142
esq = 6.69437999014 * 0.001
e1sq = 6.73949674228 * 0.001
class LocalCoord(LocalCoord_single):
ecef2ned = numpy_wrap(LocalCoord_single.ecef2ned_single, (3,), (3,))
ned2ecef = numpy_wrap(LocalCoord_single.ned2ecef_single, (3,), (3,))
geodetic2ned = numpy_wrap(LocalCoord_single.geodetic2ned_single, (3,), (3,))
ned2geodetic = numpy_wrap(LocalCoord_single.ned2geodetic_single, (3,), (3,))
def geodetic2ecef(geodetic, radians=False):
geodetic = np.array(geodetic)
input_shape = geodetic.shape
geodetic = np.atleast_2d(geodetic)
geodetic2ecef = numpy_wrap(geodetic2ecef_single, (3,), (3,))
ecef2geodetic = numpy_wrap(ecef2geodetic_single, (3,), (3,))
ratio = 1.0 if radians else (np.pi / 180.0)
lat = ratio*geodetic[:,0]
lon = ratio*geodetic[:,1]
alt = geodetic[:,2]
xi = np.sqrt(1 - esq * np.sin(lat)**2)
x = (a / xi + alt) * np.cos(lat) * np.cos(lon)
y = (a / xi + alt) * np.cos(lat) * np.sin(lon)
z = (a / xi * (1 - esq) + alt) * np.sin(lat)
ecef = np.array([x, y, z]).T
return ecef.reshape(input_shape)
def ecef2geodetic(ecef, radians=False):
"""
Convert ECEF coordinates to geodetic using ferrari's method
"""
# Save shape and export column
ecef = np.atleast_1d(ecef)
input_shape = ecef.shape
ecef = np.atleast_2d(ecef)
x, y, z = ecef[:, 0], ecef[:, 1], ecef[:, 2]
ratio = 1.0 if radians else (180.0 / np.pi)
# Conver from ECEF to geodetic using Ferrari's methods
# https://en.wikipedia.org/wiki/Geographic_coordinate_conversion#Ferrari.27s_solution
r = np.sqrt(x * x + y * y)
Esq = a * a - b * b
F = 54 * b * b * z * z
G = r * r + (1 - esq) * z * z - esq * Esq
C = (esq * esq * F * r * r) / (pow(G, 3))
S = np.cbrt(1 + C + np.sqrt(C * C + 2 * C))
P = F / (3 * pow((S + 1 / S + 1), 2) * G * G)
Q = np.sqrt(1 + 2 * esq * esq * P)
r_0 = -(P * esq * r) / (1 + Q) + np.sqrt(0.5 * a * a*(1 + 1.0 / Q) - \
P * (1 - esq) * z * z / (Q * (1 + Q)) - 0.5 * P * r * r)
U = np.sqrt(pow((r - esq * r_0), 2) + z * z)
V = np.sqrt(pow((r - esq * r_0), 2) + (1 - esq) * z * z)
Z_0 = b * b * z / (a * V)
h = U * (1 - b * b / (a * V))
lat = ratio*np.arctan((z + e1sq * Z_0) / r)
lon = ratio*np.arctan2(y, x)
# stack the new columns and return to the original shape
geodetic = np.column_stack((lat, lon, h))
return geodetic.reshape(input_shape)
class LocalCoord(object):
"""
Allows conversions to local frames. In this case NED.
That is: North East Down from the start position in
meters.
"""
def __init__(self, init_geodetic, init_ecef):
self.init_ecef = init_ecef
lat, lon, _ = (np.pi/180)*np.array(init_geodetic)
self.ned2ecef_matrix = np.array([[-np.sin(lat)*np.cos(lon), -np.sin(lon), -np.cos(lat)*np.cos(lon)],
[-np.sin(lat)*np.sin(lon), np.cos(lon), -np.cos(lat)*np.sin(lon)],
[np.cos(lat), 0, -np.sin(lat)]])
self.ecef2ned_matrix = self.ned2ecef_matrix.T
@classmethod
def from_geodetic(cls, init_geodetic):
init_ecef = geodetic2ecef(init_geodetic)
return LocalCoord(init_geodetic, init_ecef)
@classmethod
def from_ecef(cls, init_ecef):
init_geodetic = ecef2geodetic(init_ecef)
return LocalCoord(init_geodetic, init_ecef)
def ecef2ned(self, ecef):
ecef = np.array(ecef)
return np.dot(self.ecef2ned_matrix, (ecef - self.init_ecef).T).T
def ned2ecef(self, ned):
ned = np.array(ned)
# Transpose so that init_ecef will broadcast correctly for 1d or 2d ned.
return (np.dot(self.ned2ecef_matrix, ned.T).T + self.init_ecef)
def geodetic2ned(self, geodetic):
ecef = geodetic2ecef(geodetic)
return self.ecef2ned(ecef)
def ned2geodetic(self, ned):
ecef = self.ned2ecef(ned)
return ecef2geodetic(ecef)
geodetic_from_ecef = ecef2geodetic
ecef_from_geodetic = geodetic2ecef
+39 -126
View File
@@ -1,150 +1,63 @@
import numpy as np
from common.transformations.camera import eon_focal_length, \
vp_from_ke, get_view_frame_from_road_frame, \
FULL_FRAME_SIZE
from openpilot.common.transformations.orientation import rot_from_euler
from openpilot.common.transformations.camera import get_view_frame_from_calib_frame, view_frame_from_device_frame
# segnet
SEGNET_SIZE = (512, 384)
segnet_frame_from_camera_frame = np.array([
[float(SEGNET_SIZE[0])/FULL_FRAME_SIZE[0], 0., ],
[ 0., float(SEGNET_SIZE[1])/FULL_FRAME_SIZE[1]]])
# model
MODEL_INPUT_SIZE = (320, 160)
MODEL_YUV_SIZE = (MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1] * 3 // 2)
MODEL_CX = MODEL_INPUT_SIZE[0]/2.
MODEL_CY = 21.
model_zoom = 1.25
model_height = 1.22
# canonical model transform
model_intrinsics = np.array(
[[ eon_focal_length / model_zoom, 0. , MODEL_CX],
[ 0. , eon_focal_length / model_zoom, MODEL_CY],
[ 0. , 0. , 1.]])
# MED model
MEDMODEL_INPUT_SIZE = (512, 256)
MEDMODEL_YUV_SIZE = (MEDMODEL_INPUT_SIZE[0], MEDMODEL_INPUT_SIZE[1] * 3 // 2)
MEDMODEL_CY = 47.6
medmodel_zoom = 1.
medmodel_intrinsics = np.array(
[[ eon_focal_length / medmodel_zoom, 0. , 0.5 * MEDMODEL_INPUT_SIZE[0]],
[ 0. , eon_focal_length / medmodel_zoom, MEDMODEL_CY],
[ 0. , 0. , 1.]])
medmodel_fl = 910.0
medmodel_intrinsics = np.array([
[medmodel_fl, 0.0, 0.5 * MEDMODEL_INPUT_SIZE[0]],
[0.0, medmodel_fl, MEDMODEL_CY],
[0.0, 0.0, 1.0]])
# BIG model
BIGMODEL_INPUT_SIZE = (864, 288)
BIGMODEL_INPUT_SIZE = (1024, 512)
BIGMODEL_YUV_SIZE = (BIGMODEL_INPUT_SIZE[0], BIGMODEL_INPUT_SIZE[1] * 3 // 2)
bigmodel_zoom = 1.
bigmodel_intrinsics = np.array(
[[ eon_focal_length / bigmodel_zoom, 0. , 0.5 * BIGMODEL_INPUT_SIZE[0]],
[ 0. , eon_focal_length / bigmodel_zoom, 0.2 * BIGMODEL_INPUT_SIZE[1]],
[ 0. , 0. , 1.]])
bigmodel_fl = 910.0
bigmodel_intrinsics = np.array([
[bigmodel_fl, 0.0, 0.5 * BIGMODEL_INPUT_SIZE[0]],
[0.0, bigmodel_fl, 256 + MEDMODEL_CY],
[0.0, 0.0, 1.0]])
bigmodel_border = np.array([
[0,0,1],
[BIGMODEL_INPUT_SIZE[0], 0, 1],
[BIGMODEL_INPUT_SIZE[0], BIGMODEL_INPUT_SIZE[1], 1],
[0, BIGMODEL_INPUT_SIZE[1], 1],
])
# SBIG model (big model with the size of small model)
SBIGMODEL_INPUT_SIZE = (512, 256)
SBIGMODEL_YUV_SIZE = (SBIGMODEL_INPUT_SIZE[0], SBIGMODEL_INPUT_SIZE[1] * 3 // 2)
sbigmodel_fl = 455.0
sbigmodel_intrinsics = np.array([
[sbigmodel_fl, 0.0, 0.5 * SBIGMODEL_INPUT_SIZE[0]],
[0.0, sbigmodel_fl, 0.5 * (256 + MEDMODEL_CY)],
[0.0, 0.0, 1.0]])
bigmodel_frame_from_calib_frame = np.dot(bigmodel_intrinsics,
get_view_frame_from_calib_frame(0, 0, 0, 0))
model_frame_from_road_frame = np.dot(model_intrinsics,
get_view_frame_from_road_frame(0, 0, 0, model_height))
sbigmodel_frame_from_calib_frame = np.dot(sbigmodel_intrinsics,
get_view_frame_from_calib_frame(0, 0, 0, 0))
bigmodel_frame_from_road_frame = np.dot(bigmodel_intrinsics,
get_view_frame_from_road_frame(0, 0, 0, model_height))
medmodel_frame_from_calib_frame = np.dot(medmodel_intrinsics,
get_view_frame_from_calib_frame(0, 0, 0, 0))
medmodel_frame_from_road_frame = np.dot(medmodel_intrinsics,
get_view_frame_from_road_frame(0, 0, 0, model_height))
medmodel_frame_from_bigmodel_frame = np.dot(medmodel_intrinsics, np.linalg.inv(bigmodel_intrinsics))
model_frame_from_bigmodel_frame = np.dot(model_intrinsics, np.linalg.inv(bigmodel_intrinsics))
calib_from_medmodel = np.linalg.inv(medmodel_frame_from_calib_frame[:, :3])
calib_from_sbigmodel = np.linalg.inv(sbigmodel_frame_from_calib_frame[:, :3])
# 'camera from model camera'
def get_model_height_transform(camera_frame_from_road_frame, height):
camera_frame_from_road_ground = np.dot(camera_frame_from_road_frame, np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 0],
[0, 0, 1],
]))
camera_frame_from_road_high = np.dot(camera_frame_from_road_frame, np.array([
[1, 0, 0],
[0, 1, 0],
[0, 0, height - model_height],
[0, 0, 1],
]))
road_high_from_camera_frame = np.linalg.inv(camera_frame_from_road_high)
high_camera_from_low_camera = np.dot(camera_frame_from_road_ground, road_high_from_camera_frame)
return high_camera_from_low_camera
# camera_frame_from_model_frame aka 'warp matrix'
# was: calibration.h/CalibrationTransform
def get_camera_frame_from_model_frame(camera_frame_from_road_frame, height=model_height):
vp = vp_from_ke(camera_frame_from_road_frame)
model_camera_from_model_frame = np.array([
[model_zoom, 0., vp[0] - MODEL_CX * model_zoom],
[ 0., model_zoom, vp[1] - MODEL_CY * model_zoom],
[ 0., 0., 1.],
])
# This function is super slow, so skip it if height is very close to canonical
# TODO: speed it up!
if abs(height - model_height) > 0.001: #
camera_from_model_camera = get_model_height_transform(camera_frame_from_road_frame, height)
else:
camera_from_model_camera = np.eye(3)
return np.dot(camera_from_model_camera, model_camera_from_model_frame)
def get_camera_frame_from_medmodel_frame(camera_frame_from_road_frame):
camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
medmodel_frame_from_ground = medmodel_frame_from_road_frame[:, (0, 1, 3)]
ground_from_medmodel_frame = np.linalg.inv(medmodel_frame_from_ground)
camera_frame_from_medmodel_frame = np.dot(camera_frame_from_ground, ground_from_medmodel_frame)
return camera_frame_from_medmodel_frame
def get_camera_frame_from_bigmodel_frame(camera_frame_from_road_frame):
camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
bigmodel_frame_from_ground = bigmodel_frame_from_road_frame[:, (0, 1, 3)]
ground_from_bigmodel_frame = np.linalg.inv(bigmodel_frame_from_ground)
camera_frame_from_bigmodel_frame = np.dot(camera_frame_from_ground, ground_from_bigmodel_frame)
return camera_frame_from_bigmodel_frame
def get_model_frame(snu_full, camera_frame_from_model_frame, size):
idxs = camera_frame_from_model_frame.dot(np.column_stack([np.tile(np.arange(size[0]), size[1]),
np.tile(np.arange(size[1]), (size[0],1)).T.flatten(),
np.ones(size[0] * size[1])]).T).T.astype(int)
calib_flat = snu_full[idxs[:,1], idxs[:,0]]
if len(snu_full.shape) == 3:
calib = calib_flat.reshape((size[1], size[0], 3))
elif len(snu_full.shape) == 2:
calib = calib_flat.reshape((size[1], size[0]))
else:
raise ValueError("shape of input img is weird")
return calib
# This function is verified to give similar results to xx.uncommon.utils.transform_img
def get_warp_matrix(device_from_calib_euler: np.ndarray, intrinsics: np.ndarray, bigmodel_frame: bool = False) -> np.ndarray:
calib_from_model = calib_from_sbigmodel if bigmodel_frame else calib_from_medmodel
device_from_calib = rot_from_euler(device_from_calib_euler)
camera_from_calib = intrinsics @ view_frame_from_device_frame @ device_from_calib
warp_matrix: np.ndarray = camera_from_calib @ calib_from_model
return warp_matrix
+17
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#pragma once
#include <eigen3/Eigen/Dense>
#include "common/transformations/coordinates.hpp"
Eigen::Quaterniond ensure_unique(Eigen::Quaterniond quat);
Eigen::Quaterniond euler2quat(Eigen::Vector3d euler);
Eigen::Vector3d quat2euler(Eigen::Quaterniond quat);
Eigen::Matrix3d quat2rot(Eigen::Quaterniond quat);
Eigen::Quaterniond rot2quat(const Eigen::Matrix3d &rot);
Eigen::Matrix3d euler2rot(Eigen::Vector3d euler);
Eigen::Vector3d rot2euler(const Eigen::Matrix3d &rot);
Eigen::Matrix3d rot_matrix(double roll, double pitch, double yaw);
Eigen::Matrix3d rot(Eigen::Vector3d axis, double angle);
Eigen::Vector3d ecef_euler_from_ned(ECEF ecef_init, Eigen::Vector3d ned_pose);
Eigen::Vector3d ned_euler_from_ecef(ECEF ecef_init, Eigen::Vector3d ecef_pose);
+36 -279
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@@ -1,295 +1,52 @@
import numpy as np
from numpy import dot, inner, array, linalg
from common.transformations.coordinates import LocalCoord
from collections.abc import Callable
from openpilot.common.transformations.transformations import (ecef_euler_from_ned_single,
euler2quat_single,
euler2rot_single,
ned_euler_from_ecef_single,
quat2euler_single,
quat2rot_single,
rot2euler_single,
rot2quat_single)
'''
Vectorized functions that transform between
rotation matrices, euler angles and quaternions.
All support lists, array or array of arrays as inputs.
Supports both x2y and y_from_x format (y_from_x preferred!).
'''
def numpy_wrap(function, input_shape, output_shape) -> Callable[..., np.ndarray]:
"""Wrap a function to take either an input or list of inputs and return the correct shape"""
def f(*inps):
*args, inp = inps
inp = np.array(inp)
shape = inp.shape
def euler2quat(eulers):
eulers = array(eulers)
if len(eulers.shape) > 1:
output_shape = (-1,4)
else:
output_shape = (4,)
eulers = np.atleast_2d(eulers)
gamma, theta, psi = eulers[:,0], eulers[:,1], eulers[:,2]
if len(shape) == len(input_shape):
out_shape = output_shape
else:
out_shape = (shape[0],) + output_shape
q0 = np.cos(gamma / 2) * np.cos(theta / 2) * np.cos(psi / 2) + \
np.sin(gamma / 2) * np.sin(theta / 2) * np.sin(psi / 2)
q1 = np.sin(gamma / 2) * np.cos(theta / 2) * np.cos(psi / 2) - \
np.cos(gamma / 2) * np.sin(theta / 2) * np.sin(psi / 2)
q2 = np.cos(gamma / 2) * np.sin(theta / 2) * np.cos(psi / 2) + \
np.sin(gamma / 2) * np.cos(theta / 2) * np.sin(psi / 2)
q3 = np.cos(gamma / 2) * np.cos(theta / 2) * np.sin(psi / 2) - \
np.sin(gamma / 2) * np.sin(theta / 2) * np.cos(psi / 2)
# Add empty dimension if inputs is not a list
if len(shape) == len(input_shape):
inp.shape = (1, ) + inp.shape
quats = array([q0, q1, q2, q3]).T
for i in xrange(len(quats)):
if quats[i,0] < 0:
quats[i] = -quats[i]
return quats.reshape(output_shape)
result = np.asarray([function(*args, i) for i in inp])
result.shape = out_shape
return result
return f
def quat2euler(quats):
quats = array(quats)
if len(quats.shape) > 1:
output_shape = (-1,3)
else:
output_shape = (3,)
quats = np.atleast_2d(quats)
q0, q1, q2, q3 = quats[:,0], quats[:,1], quats[:,2], quats[:,3]
gamma = np.arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1**2 + q2**2))
theta = np.arcsin(2 * (q0 * q2 - q3 * q1))
psi = np.arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2**2 + q3**2))
eulers = array([gamma, theta, psi]).T
return eulers.reshape(output_shape)
def quat2rot(quats):
quats = array(quats)
input_shape = quats.shape
quats = np.atleast_2d(quats)
Rs = np.zeros((quats.shape[0], 3, 3))
q0 = quats[:, 0]
q1 = quats[:, 1]
q2 = quats[:, 2]
q3 = quats[:, 3]
Rs[:, 0, 0] = q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3
Rs[:, 0, 1] = 2 * (q1 * q2 - q0 * q3)
Rs[:, 0, 2] = 2 * (q0 * q2 + q1 * q3)
Rs[:, 1, 0] = 2 * (q1 * q2 + q0 * q3)
Rs[:, 1, 1] = q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3
Rs[:, 1, 2] = 2 * (q2 * q3 - q0 * q1)
Rs[:, 2, 0] = 2 * (q1 * q3 - q0 * q2)
Rs[:, 2, 1] = 2 * (q0 * q1 + q2 * q3)
Rs[:, 2, 2] = q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3
if len(input_shape) < 2:
return Rs[0]
else:
return Rs
def rot2quat(rots):
input_shape = rots.shape
if len(input_shape) < 3:
rots = array([rots])
K3 = np.empty((len(rots), 4, 4))
K3[:, 0, 0] = (rots[:, 0, 0] - rots[:, 1, 1] - rots[:, 2, 2]) / 3.0
K3[:, 0, 1] = (rots[:, 1, 0] + rots[:, 0, 1]) / 3.0
K3[:, 0, 2] = (rots[:, 2, 0] + rots[:, 0, 2]) / 3.0
K3[:, 0, 3] = (rots[:, 1, 2] - rots[:, 2, 1]) / 3.0
K3[:, 1, 0] = K3[:, 0, 1]
K3[:, 1, 1] = (rots[:, 1, 1] - rots[:, 0, 0] - rots[:, 2, 2]) / 3.0
K3[:, 1, 2] = (rots[:, 2, 1] + rots[:, 1, 2]) / 3.0
K3[:, 1, 3] = (rots[:, 2, 0] - rots[:, 0, 2]) / 3.0
K3[:, 2, 0] = K3[:, 0, 2]
K3[:, 2, 1] = K3[:, 1, 2]
K3[:, 2, 2] = (rots[:, 2, 2] - rots[:, 0, 0] - rots[:, 1, 1]) / 3.0
K3[:, 2, 3] = (rots[:, 0, 1] - rots[:, 1, 0]) / 3.0
K3[:, 3, 0] = K3[:, 0, 3]
K3[:, 3, 1] = K3[:, 1, 3]
K3[:, 3, 2] = K3[:, 2, 3]
K3[:, 3, 3] = (rots[:, 0, 0] + rots[:, 1, 1] + rots[:, 2, 2]) / 3.0
q = np.empty((len(rots), 4))
for i in xrange(len(rots)):
_, eigvecs = linalg.eigh(K3[i].T)
eigvecs = eigvecs[:,3:]
q[i, 0] = eigvecs[-1]
q[i, 1:] = -eigvecs[:-1].flatten()
if q[i, 0] < 0:
q[i] = -q[i]
if len(input_shape) < 3:
return q[0]
else:
return q
def euler2rot(eulers):
return rotations_from_quats(euler2quat(eulers))
def rot2euler(rots):
return quat2euler(quats_from_rotations(rots))
euler2quat = numpy_wrap(euler2quat_single, (3,), (4,))
quat2euler = numpy_wrap(quat2euler_single, (4,), (3,))
quat2rot = numpy_wrap(quat2rot_single, (4,), (3, 3))
rot2quat = numpy_wrap(rot2quat_single, (3, 3), (4,))
euler2rot = numpy_wrap(euler2rot_single, (3,), (3, 3))
rot2euler = numpy_wrap(rot2euler_single, (3, 3), (3,))
ecef_euler_from_ned = numpy_wrap(ecef_euler_from_ned_single, (3,), (3,))
ned_euler_from_ecef = numpy_wrap(ned_euler_from_ecef_single, (3,), (3,))
quats_from_rotations = rot2quat
quat_from_rot = rot2quat
rotations_from_quats = quat2rot
rot_from_quat= quat2rot
rot_from_quat= quat2rot
rot_from_quat = quat2rot
euler_from_rot = rot2euler
euler_from_quat = quat2euler
rot_from_euler = euler2rot
quat_from_euler = euler2quat
'''
Random helpers below
'''
def quat_product(q, r):
t = np.zeros(4)
t[0] = r[0] * q[0] - r[1] * q[1] - r[2] * q[2] - r[3] * q[3]
t[1] = r[0] * q[1] + r[1] * q[0] - r[2] * q[3] + r[3] * q[2]
t[2] = r[0] * q[2] + r[1] * q[3] + r[2] * q[0] - r[3] * q[1]
t[3] = r[0] * q[3] - r[1] * q[2] + r[2] * q[1] + r[3] * q[0]
return t
def rot_matrix(roll, pitch, yaw):
cr, sr = np.cos(roll), np.sin(roll)
cp, sp = np.cos(pitch), np.sin(pitch)
cy, sy = np.cos(yaw), np.sin(yaw)
rr = array([[1,0,0],[0, cr,-sr],[0, sr, cr]])
rp = array([[cp,0,sp],[0, 1,0],[-sp, 0, cp]])
ry = array([[cy,-sy,0],[sy, cy,0],[0, 0, 1]])
return ry.dot(rp.dot(rr))
def rot(axis, angle):
# Rotates around an arbitrary axis
ret_1 = (1 - np.cos(angle)) * array([[axis[0]**2, axis[0] * axis[1], axis[0] * axis[2]], [
axis[1] * axis[0], axis[1]**2, axis[1] * axis[2]
], [axis[2] * axis[0], axis[2] * axis[1], axis[2]**2]])
ret_2 = np.cos(angle) * np.eye(3)
ret_3 = np.sin(angle) * array([[0, -axis[2], axis[1]], [axis[2], 0, -axis[0]],
[-axis[1], axis[0], 0]])
return ret_1 + ret_2 + ret_3
def ecef_euler_from_ned(ned_ecef_init, ned_pose):
'''
Got it from here:
Using Rotations to Build Aerospace Coordinate Systems
-Don Koks
'''
converter = LocalCoord.from_ecef(ned_ecef_init)
x0 = converter.ned2ecef([1, 0, 0]) - converter.ned2ecef([0, 0, 0])
y0 = converter.ned2ecef([0, 1, 0]) - converter.ned2ecef([0, 0, 0])
z0 = converter.ned2ecef([0, 0, 1]) - converter.ned2ecef([0, 0, 0])
x1 = rot(z0, ned_pose[2]).dot(x0)
y1 = rot(z0, ned_pose[2]).dot(y0)
z1 = rot(z0, ned_pose[2]).dot(z0)
x2 = rot(y1, ned_pose[1]).dot(x1)
y2 = rot(y1, ned_pose[1]).dot(y1)
z2 = rot(y1, ned_pose[1]).dot(z1)
x3 = rot(x2, ned_pose[0]).dot(x2)
y3 = rot(x2, ned_pose[0]).dot(y2)
#z3 = rot(x2, ned_pose[0]).dot(z2)
x0 = array([1, 0, 0])
y0 = array([0, 1, 0])
z0 = array([0, 0, 1])
psi = np.arctan2(inner(x3, y0), inner(x3, x0))
theta = np.arctan2(-inner(x3, z0), np.sqrt(inner(x3, x0)**2 + inner(x3, y0)**2))
y2 = rot(z0, psi).dot(y0)
z2 = rot(y2, theta).dot(z0)
phi = np.arctan2(inner(y3, z2), inner(y3, y2))
ret = array([phi, theta, psi])
return ret
def ned_euler_from_ecef(ned_ecef_init, ecef_poses):
'''
Got the math from here:
Using Rotations to Build Aerospace Coordinate Systems
-Don Koks
Also accepts array of ecef_poses and array of ned_ecef_inits.
Where each row is a pose and an ecef_init.
'''
ned_ecef_init = array(ned_ecef_init)
ecef_poses = array(ecef_poses)
output_shape = ecef_poses.shape
ned_ecef_init = np.atleast_2d(ned_ecef_init)
if ned_ecef_init.shape[0] == 1:
ned_ecef_init = np.tile(ned_ecef_init[0], (output_shape[0], 1))
ecef_poses = np.atleast_2d(ecef_poses)
ned_poses = np.zeros(ecef_poses.shape)
for i, ecef_pose in enumerate(ecef_poses):
converter = LocalCoord.from_ecef(ned_ecef_init[i])
x0 = array([1, 0, 0])
y0 = array([0, 1, 0])
z0 = array([0, 0, 1])
x1 = rot(z0, ecef_pose[2]).dot(x0)
y1 = rot(z0, ecef_pose[2]).dot(y0)
z1 = rot(z0, ecef_pose[2]).dot(z0)
x2 = rot(y1, ecef_pose[1]).dot(x1)
y2 = rot(y1, ecef_pose[1]).dot(y1)
z2 = rot(y1, ecef_pose[1]).dot(z1)
x3 = rot(x2, ecef_pose[0]).dot(x2)
y3 = rot(x2, ecef_pose[0]).dot(y2)
#z3 = rot(x2, ecef_pose[0]).dot(z2)
x0 = converter.ned2ecef([1, 0, 0]) - converter.ned2ecef([0, 0, 0])
y0 = converter.ned2ecef([0, 1, 0]) - converter.ned2ecef([0, 0, 0])
z0 = converter.ned2ecef([0, 0, 1]) - converter.ned2ecef([0, 0, 0])
psi = np.arctan2(inner(x3, y0), inner(x3, x0))
theta = np.arctan2(-inner(x3, z0), np.sqrt(inner(x3, x0)**2 + inner(x3, y0)**2))
y2 = rot(z0, psi).dot(y0)
z2 = rot(y2, theta).dot(z0)
phi = np.arctan2(inner(y3, z2), inner(y3, y2))
ned_poses[i] = array([phi, theta, psi])
return ned_poses.reshape(output_shape)
def ecef2car(car_ecef, psi, theta, points_ecef, ned_converter):
"""
TODO: add roll rotation
Converts an array of points in ecef coordinates into
x-forward, y-left, z-up coordinates
Parameters
----------
psi: yaw, radian
theta: pitch, radian
Returns
-------
[x, y, z] coordinates in car frame
"""
# input is an array of points in ecef cocrdinates
# output is an array of points in car's coordinate (x-front, y-left, z-up)
# convert points to NED
points_ned = []
for p in points_ecef:
points_ned.append(ned_converter.ecef2ned_matrix.dot(array(p) - car_ecef))
points_ned = np.vstack(points_ned).T
# n, e, d -> x, y, z
# Calculate relative postions and rotate wrt to heading and pitch of car
invert_R = array([[1., 0., 0.], [0., -1., 0.], [0., 0., -1.]])
c, s = np.cos(psi), np.sin(psi)
yaw_R = array([[c, s, 0.], [-s, c, 0.], [0., 0., 1.]])
c, s = np.cos(theta), np.sin(theta)
pitch_R = array([[c, 0., -s], [0., 1., 0.], [s, 0., c]])
return dot(pitch_R, dot(yaw_R, dot(invert_R, points_ned)))
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# cython: language_level=3
from libcpp cimport bool
cdef extern from "orientation.cc":
pass
cdef extern from "orientation.hpp":
cdef cppclass Quaternion "Eigen::Quaterniond":
Quaternion()
Quaternion(double, double, double, double)
double w()
double x()
double y()
double z()
cdef cppclass Vector3 "Eigen::Vector3d":
Vector3()
Vector3(double, double, double)
double operator()(int)
cdef cppclass Matrix3 "Eigen::Matrix3d":
Matrix3()
Matrix3(double*)
double operator()(int, int)
Quaternion euler2quat(Vector3)
Vector3 quat2euler(Quaternion)
Matrix3 quat2rot(Quaternion)
Quaternion rot2quat(Matrix3)
Vector3 rot2euler(Matrix3)
Matrix3 euler2rot(Vector3)
Matrix3 rot_matrix(double, double, double)
Vector3 ecef_euler_from_ned(ECEF, Vector3)
Vector3 ned_euler_from_ecef(ECEF, Vector3)
cdef extern from "coordinates.cc":
cdef struct ECEF:
double x
double y
double z
cdef struct NED:
double n
double e
double d
cdef struct Geodetic:
double lat
double lon
double alt
bool radians
ECEF geodetic2ecef(Geodetic)
Geodetic ecef2geodetic(ECEF)
cdef cppclass LocalCoord_c "LocalCoord":
Matrix3 ned2ecef_matrix
Matrix3 ecef2ned_matrix
LocalCoord_c(Geodetic, ECEF)
LocalCoord_c(Geodetic)
LocalCoord_c(ECEF)
NED ecef2ned(ECEF)
ECEF ned2ecef(NED)
NED geodetic2ned(Geodetic)
Geodetic ned2geodetic(NED)
cdef extern from "coordinates.hpp":
pass
+173
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# distutils: language = c++
# cython: language_level = 3
from openpilot.common.transformations.transformations cimport Matrix3, Vector3, Quaternion
from openpilot.common.transformations.transformations cimport ECEF, NED, Geodetic
from openpilot.common.transformations.transformations cimport euler2quat as euler2quat_c
from openpilot.common.transformations.transformations cimport quat2euler as quat2euler_c
from openpilot.common.transformations.transformations cimport quat2rot as quat2rot_c
from openpilot.common.transformations.transformations cimport rot2quat as rot2quat_c
from openpilot.common.transformations.transformations cimport euler2rot as euler2rot_c
from openpilot.common.transformations.transformations cimport rot2euler as rot2euler_c
from openpilot.common.transformations.transformations cimport rot_matrix as rot_matrix_c
from openpilot.common.transformations.transformations cimport ecef_euler_from_ned as ecef_euler_from_ned_c
from openpilot.common.transformations.transformations cimport ned_euler_from_ecef as ned_euler_from_ecef_c
from openpilot.common.transformations.transformations cimport geodetic2ecef as geodetic2ecef_c
from openpilot.common.transformations.transformations cimport ecef2geodetic as ecef2geodetic_c
from openpilot.common.transformations.transformations cimport LocalCoord_c
import numpy as np
cimport numpy as np
cdef np.ndarray[double, ndim=2] matrix2numpy(Matrix3 m):
return np.array([
[m(0, 0), m(0, 1), m(0, 2)],
[m(1, 0), m(1, 1), m(1, 2)],
[m(2, 0), m(2, 1), m(2, 2)],
])
cdef Matrix3 numpy2matrix(np.ndarray[double, ndim=2, mode="fortran"] m):
assert m.shape[0] == 3
assert m.shape[1] == 3
return Matrix3(<double*>m.data)
cdef ECEF list2ecef(ecef):
cdef ECEF e
e.x = ecef[0]
e.y = ecef[1]
e.z = ecef[2]
return e
cdef NED list2ned(ned):
cdef NED n
n.n = ned[0]
n.e = ned[1]
n.d = ned[2]
return n
cdef Geodetic list2geodetic(geodetic):
cdef Geodetic g
g.lat = geodetic[0]
g.lon = geodetic[1]
g.alt = geodetic[2]
return g
def euler2quat_single(euler):
cdef Vector3 e = Vector3(euler[0], euler[1], euler[2])
cdef Quaternion q = euler2quat_c(e)
return [q.w(), q.x(), q.y(), q.z()]
def quat2euler_single(quat):
cdef Quaternion q = Quaternion(quat[0], quat[1], quat[2], quat[3])
cdef Vector3 e = quat2euler_c(q)
return [e(0), e(1), e(2)]
def quat2rot_single(quat):
cdef Quaternion q = Quaternion(quat[0], quat[1], quat[2], quat[3])
cdef Matrix3 r = quat2rot_c(q)
return matrix2numpy(r)
def rot2quat_single(rot):
cdef Matrix3 r = numpy2matrix(np.asfortranarray(rot, dtype=np.double))
cdef Quaternion q = rot2quat_c(r)
return [q.w(), q.x(), q.y(), q.z()]
def euler2rot_single(euler):
cdef Vector3 e = Vector3(euler[0], euler[1], euler[2])
cdef Matrix3 r = euler2rot_c(e)
return matrix2numpy(r)
def rot2euler_single(rot):
cdef Matrix3 r = numpy2matrix(np.asfortranarray(rot, dtype=np.double))
cdef Vector3 e = rot2euler_c(r)
return [e(0), e(1), e(2)]
def rot_matrix(roll, pitch, yaw):
return matrix2numpy(rot_matrix_c(roll, pitch, yaw))
def ecef_euler_from_ned_single(ecef_init, ned_pose):
cdef ECEF init = list2ecef(ecef_init)
cdef Vector3 pose = Vector3(ned_pose[0], ned_pose[1], ned_pose[2])
cdef Vector3 e = ecef_euler_from_ned_c(init, pose)
return [e(0), e(1), e(2)]
def ned_euler_from_ecef_single(ecef_init, ecef_pose):
cdef ECEF init = list2ecef(ecef_init)
cdef Vector3 pose = Vector3(ecef_pose[0], ecef_pose[1], ecef_pose[2])
cdef Vector3 e = ned_euler_from_ecef_c(init, pose)
return [e(0), e(1), e(2)]
def geodetic2ecef_single(geodetic):
cdef Geodetic g = list2geodetic(geodetic)
cdef ECEF e = geodetic2ecef_c(g)
return [e.x, e.y, e.z]
def ecef2geodetic_single(ecef):
cdef ECEF e = list2ecef(ecef)
cdef Geodetic g = ecef2geodetic_c(e)
return [g.lat, g.lon, g.alt]
cdef class LocalCoord:
cdef LocalCoord_c * lc
def __init__(self, geodetic=None, ecef=None):
assert (geodetic is not None) or (ecef is not None)
if geodetic is not None:
self.lc = new LocalCoord_c(list2geodetic(geodetic))
elif ecef is not None:
self.lc = new LocalCoord_c(list2ecef(ecef))
@property
def ned2ecef_matrix(self):
return matrix2numpy(self.lc.ned2ecef_matrix)
@property
def ecef2ned_matrix(self):
return matrix2numpy(self.lc.ecef2ned_matrix)
@property
def ned_from_ecef_matrix(self):
return self.ecef2ned_matrix
@property
def ecef_from_ned_matrix(self):
return self.ned2ecef_matrix
@classmethod
def from_geodetic(cls, geodetic):
return cls(geodetic=geodetic)
@classmethod
def from_ecef(cls, ecef):
return cls(ecef=ecef)
def ecef2ned_single(self, ecef):
assert self.lc
cdef ECEF e = list2ecef(ecef)
cdef NED n = self.lc.ecef2ned(e)
return [n.n, n.e, n.d]
def ned2ecef_single(self, ned):
assert self.lc
cdef NED n = list2ned(ned)
cdef ECEF e = self.lc.ned2ecef(n)
return [e.x, e.y, e.z]
def geodetic2ned_single(self, geodetic):
assert self.lc
cdef Geodetic g = list2geodetic(geodetic)
cdef NED n = self.lc.geodetic2ned(g)
return [n.n, n.e, n.d]
def ned2geodetic_single(self, ned):
assert self.lc
cdef NED n = list2ned(ned)
cdef Geodetic g = self.lc.ned2geodetic(n)
return [g.lat, g.lon, g.alt]
def __dealloc__(self):
del self.lc
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