Displaying 1 to 15 from 15 results

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.

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.

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.




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).

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.