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 two ways you can build and run Vision Pilot - we provide example data which you can use for visualization and evaluation, select either Option-1 or Option-2 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.git

Download 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 ../

  make

This will build the project and create VisionPilot executable inside the build directory.

Run Vision Pilot on test data and visualize outputs

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

Build VisionPilot DEB package

To build Debian DEB package for VisionPilot run

  cpack -G DEB

To build Debian DEB package for VisionPilot with CPU support build VisionPilot with

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

  make

  cpack -G DEB

Option 2 - Use the pre-built Debian pacakge

Install from the pre-built Debian package

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.deb

Reboot the system, cuda dependencies for VisionPilot installed.

Run Vision Pilot on test data and visualize outputs

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

Run inside 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 --cpu

To run the container use the run.sh script. For example to run the container with CPU support

  ./run.sh --cpu

or with GPU support and ROS2 support

  ./run.sh --gpu --ros2

Note: 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.

Roadmap

• Guide on how to run Vision Pilot with simulators
• Containerization of Vision Pilot
• Guide on how to run Vision Pilot with your own camera
• Support for 8MP camera resolution with 120 degree horizontal field-of-view
• Support for fusion between front-facing camera and automotive RADAR
• Safety Verification and Automotive Standards Compliance (ISO26262, ISO8800)

Contributing

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