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This codebase contains a productionizable and safety certifiable implementation of an open-source L2 ADAS system called Vision Pilot.
Vision Pilot is designed to be integrated with automotive OEMs and Tier-1 suppliers in series production passenger cars, and the system can optionally be adopted for transportation and logisitcs use-cases in buses and trucks.
We offer the complete codebase as free and fully open-source, including AI model weights to help democratize access to this vital technology. Vision Pilot is available under the persmissive Apache 2.0 licence and can freely be used for both commercial and research purposes.
Vision Pilot is designed to support basic/entry L2 ADAS features for in-lane autonomous driving including the following features:
- ACC - autonomous cruise control
- FCW - forward collision warning
- AEB - autonomous emergency braking
- LKAS - lane keep assist
- LDW - lane departure warning
- ISA - intelligent speed assist
- Autopilot - single-lane hands-free highway autopilot
Sensor specification:
Vision Pilot can be run with a single, front-facing, monocular camera with 52 - 55 degree horizontal field-of-view, and 1MP - 2MP resolution.
We utilize a Hybrid End-to-End AI Architecture as the core of Vision Pilot in which data is processed in parallel by perception AI models for safety, and End-to-End AI models for performance.
Specifically, Vision Pilot leverages three open-source AI models developed by the Autoware Foundation, these are:
- AutoSpeed - Closest in-path object detection
- AutoSteer - Ego path future waypoint detection
- AutoDrive - End to End distance/in-path object presence detection and road curvature estimation
Vision Pilot does not require 3D high definition maps and operates in a 'mapless' mode to follow the road in real-time.
For more information about Vision Pilot, please view our introductory presentation at this link
There are three ways you can build and run Vision Pilot - we provide example data which you can use for visualization and evaluation, select from the drop down options below:
Option 1 - Download and build from source
To get started with the project, download the source code from:
git clone https://github.com/autowarefoundation/autoware_vision_pilot.gitDownload ONNX Runtime from the GitHub releases page.
Build the project:
cd VisionPilot mkdir build && cd build cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> ../or with ROS2 support
cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DENABLE_ROS2_INTERFACE=ON ../ makeThis will build the project and create VisionPilot executable inside the build directory.
OpenLane Dataset:
To test Vision Pilot using open loop scenario testing, first download the sample data from the Google Drive directory.
This directory contains video composed of image data from sequences in the appropriate dataset and vehicle speed data extracted from the dataset.
Update VisionPilot config files vision_pilot.conf inside config directory and set:
source.mode = video
and vision_pilot_test.conf set:
source.input_video = <INPUT_VIDEO_FILE_PATH>
source.input_vehicle_speed = <INPUT_VEHICLE_SPEED_FILE_PATH>
to point to the appropriate video file path and vehicle speed file path.
Note: When VisionPilot built from source, update config files before the build.
Run VisionPilot from inside build directory
./VisionPilot
To build Debian DEB package for VisionPilot run
cpack -G DEBTo build Debian DEB package for VisionPilot with CPU support build VisionPilot with
cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DGPU=OFF ../ make cpack -G DEBOption 2 - Use the pre-built Debian pacakge
This method is recommended in case new system installation and cuda dependencies are not installed yet.
Download VisionPilot prebuilt binary.
Install the .deb package
sudo apt install ./VisionPilot-1.0-x86_64.debReboot the system, cuda dependencies for VisionPilot installed.
OpenLane Dataset:
To test Vision Pilot using open loop scenario testing, first download the sample data from the Google Drive directory.
This directory contains video composed of image data from sequences in the appropriate dataset and vehicle speed data extracted from the dataset.
Update VisionPilot config files vision_pilot.conf set:
source.mode = video
and vision_pilot_test.conf set:
source.input_video = <INPUT_VIDEO_FILE_PATH>
source.input_vehicle_speed = <INPUT_VEHICLE_SPEED_FILE_PATH>
to point to the appropriate video file path and vehicle speed file path.
Note: When VisionPilot installed from prebuilt binary update the config files inside:
/usr/share/visionpilot/config
directory.
Run VisionPilot from the command line
VisionPilot
Option 3 - Use the Docker container
To run Vision Pilot inside a Docker container, build the container using the Dockerfiles provided in the docker directory of the repository. Docker containers can be built with GPU/CPU support, and NO_ROS2/ROS2 support.
To build the container, go to the docker subdirectory and run the following commands:
Default is GPU support NO ROS2 support
./build.sh --gpu --ros2 to build with CPU support
./build.sh --cpuTo run the container use the run.sh script. For example to run the container with CPU support
./run.sh --cpuor with GPU support and ROS2 support
./run.sh --gpu --ros2Note: Before building the container update the config files inside: config directory.
If building with CPU support update config/vision_pilot.conf
engine.provider = cpu
and when building with ros2 support update
source.mode = ros2
When using video input source update config/vision_pilot_test.conf
source.input_video = <INPUT_VIDEO_FILE_PATH>
source.input_vehicle_speed = <INPUT_VEHICLE_SPEED_FILE_PATH>
Input directory can be modified in the source by using run.sh script switch
./run.sh --gpu --data <HOST_DIR>:<CONTAINER_DIR>Note: <CONTAINER_DIR> need to be the same as during the container build.
We support the CARLA Simulator version 0.9.16 for closed-loop testing of Vision Pilot in a virtual environment
CARLA 0.9.16 with Unreal Engine 4: https://carla.readthedocs.io/en/latest/
-
Download binaries and dependencies following official documentation: https://carla-ue5.readthedocs.io/en/latest/start_quickstart/#
-
If your GPU has 6GB or less of VRAM then you should please follow the modifications specified in https://gist.github.com/xmfcx/a5e32fdecfcd85c6cc9d472ce7a3a98d to run CARLA with lower VRAM requirements using docker (tested with RTX3060 laptop version)
Change the file path below to where CARLA is downloaded and run. Make sure to change the --volume path to your local
specific directory path.
docker run -it --rm \
--runtime=nvidia \ # Use NVIDIA runtime for GPU access
--net=host \ # Use the host's network stack (helps with networking/performance)
--env=DISPLAY=$DISPLAY \ # Pass the host's DISPLAY environment variable (for GUI forwarding)
--env=NVIDIA_VISIBLE_DEVICES=all \ # Expose all GPUs to the container
--env=NVIDIA_DRIVER_CAPABILITIES=all \ # Enable all driver capabilities (graphics, compute, etc.)
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \ # Mount X11 UNIX socket to enable GUI apps to display
--volume="$HOME/Downloads/carla/CARLA_0.9.16/:/home/carla/host-carla" \
# CHANGE AS NEEDED: Mount your local CARLA folder into the container
--workdir="/home/carla/host-carla" \ # Set the working directory to the mounted CARLA folder
carlasim/carla:0.9.16 \ # Use the official CARLA Docker image, version 0.9.16
bash CarlaUE4.sh -nosound # Run the CARLA startup script with -nosound flagTo run with ROS2 native interface, add --ros2 at the end
Update vision_pilot.conf set source.mode
source.mode = ros2 and also vision_pilot_ros2.conf set ource.input_camera_topic
source.input_camera_topic = /carla/hero/main_cam/imageBuild VisionPilot with ROS2 support
cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DENABLE_ROS2_INTERFACE=ON ../Go to Simulation/CARLA/ROS2 directory and build the CARLA ROS2 bridge
colcon buildSource the installation directory
source ./install/setup.bash ros2 launch carla_bridge_bringup carla_bridge.launch.py host:=<HOST> port:=<PORT>Note: If you are running CARLA on a different machine to the one which is running Vision Pilot you need to specify the host and port parameters. Set the IP address of the machine running CARLA as a host and set the port on which CARLA is publishing as a port.
Go to VisionPilot build directory and run the VisionPilot
./VisionPilot
In order to run Vision Pilot with your own camera, you will need to calibrate your camera and provide the calibraiton information to the Vision Pilot app - this is important for Vision Pilot to accurately measure the shape of the road and the distance to objects.
Please follow the steps in the calibration guide and save the calibration yaml file in the Vision Pilot config folder and overwrite the data of the H.yaml file with your calibraiton file parameters - we suggest you retain a copy of the original H.yaml parameters so that you can run Vision Pilot on our example data.
- Support for fusion between front-facing camera and automotive RADAR
- Support for 8MP camera resolution with 120 degree horizontal field-of-view
- Safety Verification and Automotive Standards Compliance (ISO26262, ISO8800)
To learn more about how to participate in this project, please read the onboarding guide