Vision Pilot - Open Source L2 ADAS

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Free and fully open-source stack for L2 ADAS

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.

Hybrid End-to-End AI Architecture

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:

1. AutoSpeed - Closest in-path object detection
2. AutoSteer - Ego path future waypoint detection
3. AutoDrive - End to End distance/in-path object presence detection and road curvature estimation

No Reliance on High Definition Maps

Vision Pilot does not require 3D high definition maps and operates in a 'mapless' mode to follow the road in real-time.

Presentation

For more information about Vision Pilot, please view our introductory presentation at this link

Getting Started

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:

  git clone https://github.com/autowarefoundation/autoware_vision_pilot.git

  cd VisionPilot

  mkdir build && cd build

  cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> ../

  cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DENABLE_ROS2_INTERFACE=ON ../

  cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DENABLE_OCCUPANCY=ON ../

  make

source.mode             = video

source.input_video         = <INPUT_VIDEO_FILE_PATH>
source.input_vehicle_speed = <INPUT_VEHICLE_SPEED_FILE_PATH>

./VisionPilot

  cpack -G DEB

  cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DGPU=OFF ../

  make

  cpack -G DEB

  sudo apt install ./VisionPilot-1.0-x86_64.deb

source.mode             = video

source.input_video         = <INPUT_VIDEO_FILE_PATH>
source.input_vehicle_speed = <INPUT_VEHICLE_SPEED_FILE_PATH>

/usr/share/visionpilot/config

VisionPilot

  ./build.sh --gpu --ros2 

  ./build.sh --cpu

  ./build.sh --gpu --occupancy

  ./run.sh --cpu

  ./run.sh --gpu --ros2

engine.provider     = cpu

source.mode         = ros2

source.input_video         = <INPUT_VIDEO_FILE_PATH>
source.input_vehicle_speed = <INPUT_VEHICLE_SPEED_FILE_PATH>

  ./run.sh --gpu --data <HOST_DIR>:<CONTAINER_DIR>

Running Vision Pilot in a Simulator

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/

How to Install CARLA Simulator

1. Download binaries and dependencies following official documentation: https://carla-ue5.readthedocs.io/en/latest/start_quickstart/#

2. 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)

How to run CARLA Simulator with Vision Pilot

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 flag

To run with ROS2 native interface, add --ros2 at the end

Update VisionPilot config

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/image

Build VisionPilot

Build VisionPilot with ROS2 support

  cmake -DONNXRUNTIME_ROOT=<ONNX_RUNTIME_ROOT_PATH> -DENABLE_ROS2_INTERFACE=ON ../

Build CARLA bridge

Go to Simulation/CARLA/ROS2 directory and build the CARLA ROS2 bridge

  colcon build

Run CARLA Bridge

Source 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.

Run VisionPilot

Go to VisionPilot build directory and run the VisionPilot

  ./VisionPilot

Running Vision Pilot with your own camera

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.

Calibrate your camera

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.

Roadmap

• 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)

Contributing

To learn more about how to participate in this project, please read the onboarding guide