apollo - An open autonomous driving platform

•    C++

Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles. For business and partnership, please visit our website.

PythonRobotics - Python sample codes for robotics algorithms.

•    Python

Python codes for robotics algorithm. This is a Python code collection of robotics algorithms, especially for autonomous navigation.

carla - Open-source simulator for autonomous driving research.

•    C++

CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.

webots - Webots Robot Simulator

•    C++

Webots is an open-source robot simulator released under the terms of the Apache 2.0 license. It provides a complete development environment to model, program and simulate robots, vehicles and biomechanical systems. You can download pre-compiled binaries for Windows, macOS and Linux of the latest release, as well as older releases and nightly builds.

autoware.ai - Open-source software for self-driving vehicles

Autoware is the world's first "all-in-one" open-source software for self-driving vehicles. The capabilities of Autoware are primarily well-suited for urban cities, but highways, freeways, mesomountaineous regions, and geofenced areas can be also covered. The code base of Autoware is protected by the Apache 2 License. Please use it at your own discretion. For safe use, we provide a ROSBAG-based simulation environment for those who do not own real autonomous vehicles. If you plan to use Autoware with real autonomous vehicles, please formulate safety measures and assessment of risk before field testing. You may refer to Autoware Wiki for Users Guide and Developers Guide.

AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

•    C++

AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.

apm_planner - APM Planner Ground Control Station (Qt)

•    C++

APM Planner Ground Control Station (Qt)

SqueezeSeg - Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation

•    Python

SqueezeSeg is released under the BSD license (See LICENSE for details). The dataset used for training, evaluation, and demostration of SqueezeSeg is modified from KITTI raw dataset. For your convenience, we provide links to download the converted dataset, which is distrubited under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. The instructions are tested on Ubuntu 16.04 with python 2.7 and tensorflow 1.0 with GPU support.

carma-platform - CARMA Platform is built on robot operating system (ROS) and utilizes open source software (OSS) that enables Cooperative Driving Automation (CDA) features to allow Automated Driving Systems to interact and cooperate with infrastructure and other vehicles through communication

•    C++

CARMASM advances research and development to accelerate market readiness and deployment of cooperative driving automation, while advancing automated driving technology safety, security, data, and artificial intelligence. It encourages collaboration and participation by a community of engineers and researchers to advance understanding of cooperative driving automation using open source software (OSS) and agile project management practices. CARMA is a reusable, extensible platform for controlling SAE level 2 connected, automated vehicles (AVs). It provides a rich, generic API for third party plugins that implement vehicle guidance algorithms to plan vehicle trajectories. It is written in C++ and runs in a Robot Operating System (ROS) environment on Ubuntu. The platform can be reused on a variety of properly equipped vehicles. Migration has begun from the ROS 1 framework to ROS 2.

burro - Platform for small-scale self-driving vehicles.

•    Python

Burro is a platform for small-scale self-driving cars. Using Burro you can build either a RC-style Ackermann steering car, or a Differential steering car (like a three-wheel robot). Depending on your hardware Burro will automatically select and setup the right kind of vehicle each time you run it. Thus you may share the same SD card among different vehicles without any changes.

AutonomousDrivingCookbook - Scenarios, tutorials and demos for Autonomous Driving

•    Jupyter

This project is developed and being maintained by the Microsoft Deep Learning and Robotics Garage Chapter. This is currently a work in progress. We will continue to add more tutorials and scenarios based on requests from our users and the availability of our collaborators.Autonomous Driving has transcended far beyond being a crazy moonshot idea over the last half decade or so. It is quickly becoming the biggest technology today that promises to shape our tomorrow, not very unlike when cars came into existence in the first place. Almost every single car manufacturer, every big technology company, and a number of very promising startups have been working on different aspects of autonomous driving to help shape this revolution. Some of the biggest drivers powering this change have been the recent advances in software (robotics and deep learning techniques), hardware technology (GPUs, FPGAs etc.) and cloud computing. Cloud platforms like Azure have enabled ingest and processing of large amounts of data, making it possible for companies to push for levels 4 and 5 of AD autonomy.

bht-ams-playerstage - Player/Stage SLAM

•    C

A homework solution for the Autonomous Mobile Systems class at Beuth Hochschule für Technik, Berlin with Prof. Dr. Volker Sommer. Runs a simulation of a VolksBot with (error-free) laser rangers. in the project directory to bootstrap a virtual machine that is preconfigured with Player/Stage. The VM will be downloaded if it doesn't already exist your local machine (which is likely if you run the command the first time).

HybridAStarTrailer - A path planning algorithm based on Hybrid A* for trailer truck

•    Julia

A path planning algorithm based on Hybrid A* for trailer truck. I want to achieve this on autonomous vehicle (click the image to see movie).

MATLABRobotics - MATLAB sample codes for mobile robot navigation

•    Matlab

MATLAB sample codes for mobile robot navigation. Sample codes for localization.

open-basemap - More details at:

•    Lasso

Open Basemap is a collaborative initiative towards enabling worldwide autonomous vehicle development. More information can be found here. Besides git you will also need to setup git-lfs on your system by following the instructions here.

ad-rss-lib - Library implementing the Responsibility Sensitive Safety model (RSS) for Autonomous Vehicles

•    C++

This library intends to provide a C++ implementation of the Responsibility Sensitive Safety model (RSS) for Autonomous Vehicles. RSS is described in the following papers. Potential users of this C++ library are encouraged to read these papers in order to become familiar with the concepts and functions provided by the library.

metacar - A reinforcement learning environment for self-driving cars in the browser.

•    TypeScript

Metacar is a 2D reinforcement learning environment for autonomous vehicles running in the browser. The project aims to let reinforcement learning be more accessible to everyone through solving fun problems. Metacar comes with a set of a predefined levels, some harder to address than others. More levels and possibile scenarios will be added soon (pedestrian, bikes...). Furthermore, the library let you create your own levels and personalize the environment to create your desired scenario. You can also take a look at the online demo.

GPS_INS_Fusion - Fusing GPS, IMU and Encoder sensors for accurate state estimation.

•    C++

EKF to fuse GPS, IMU and encoder readings to estimate the pose of a ground robot in the navigation frame. Wikipedia writes: In the extended Kalman filter, the state transition and observation models need not be linear functions of the state but may instead be differentiable functions.

highway-path-planning - My path-planning pipeline to navigate a car safely around a virtual highway with other traffic

•    C++

This is my submission to the path planning project for term 3 of Udacity's self-driving car program. The goal is to create a path planning pipeline that would smartly, safely, and comfortably navigate a virtual car around a virtual highway with other traffic. We are given a map of the highway, as well as sensor fusion and localization data about our car and nearby cars. We are supposed to give back a set of map points (x, y) that a perfect controller will execute every 0.02 seconds. Navigating safely and comfortably means we don't bump into other cars, we don't exceed the maximum speed, acceleration and jerk requirements. Navigating smartly means we change lanes when we have to. Clone this repo.

Electron - An autonomous deep learning indoor delivery robot made with Jetson

•    C++

This is the GitHub repo for Electron, an indoor delivery robot. The goal of the robot is to deliver items to cubicles in an office building, be it office supplies or food. The robot must be able to autonomously drive around a building while successfully retrieving and delivering these items constantly upon user request. We do this using ROS, deep learning, and various sensors. In order to move around a building autonomously, our robot creates a map of the building using SLAM (gmapping) with an RPLidar A1 and odometry. It later utilizes the same map to navigate throughout the building by localizing itself using AMCL (adaptive monte carlo localization). We do most of this using ROS (Robot Operating System) and RVIZ for visualization.