AutoDRIVE


Website	Paper	YouTube	GitHub

Project Overview

AutoDRIVE is envisioned to be an integrated platform for autonomous driving research and education. It bridges the gap between software simulation and hardware deployment by providing the AutoDRIVE Simulator and AutoDRIVE Testbed, a well-suited duo for sim2real applications. It also offers AutoDRIVE Devkit, a developer's kit for rapid and flexible development of autonomy algorithms. Although the platform is primarily targeted towards autonomous driving, it also supports the development of smart-city solutions for managing the traffic flow.

AutoDRIVE Testbed


Vehicle	Infrastructure

AutoDRIVE Testbed is the hardware setup comprising of a scaled vehicle model (called Nigel) and a modular infrastructure development kit. The vehicle is equipped with a comprehensive sensor suite for redundant perception, a set of actuators for constrained motion control and a fully functional lighting system for illumination and signaling. It can be teleoperated (in manual mode) or self-driven (in autonomous mode). The infrastructure development kit comprises of various environment modules along with active and passive traffic elements.

Source Branch: AutoDRIVE Testbed
Latest Release: AutoDRIVE Testbed 0.2.0
Upcoming Release: AutoDRIVE Testbed 0.3.0 is currently under development.
Nigel (AS) Build Documentation: Assembly Guide, Assembly Animation and BOM
Nigel (4WD4WS) Build Documentation: Assembly Guide, Assembly Animation and BOM

AutoDRIVE Simulator


Vehicle	Infrastructure

AutoDRIVE Simulator is the digital twin of the AutoDRIVE Testbed, which enables the users to virtually prototype their algorithms either due to hardware limitations or as a part of the reiterative development cycle. It is developed atop the Unity game engine and offers a WebSocket interface for bilateral communication with the autonomy algorithms developed independently using the AutoDRIVE Devkit. The standalone simulator application is targeted at Full HD resolution (1920x1080p) with cross-platform support (Windows, macOS and Linux). It is a light-weight software application that utilizes system resources wisely. This enables deployment of the simulator application and autonomy algorithms on a single machine; nonetheless, distributed computing is also supported.

Source Branch: AutoDRIVE Simulator
Latest Release: AutoDRIVE Simulator 0.3.0
Upcoming Release: AutoDRIVE Simulator 0.4.0 is currently under development.

AutoDRIVE Devkit


ADSS Toolkit	SCSS Toolkit

AutoDRIVE Devkit is a developer's kit that enables the users to exploit AutoDRIVE Simulator or AutoDRIVE Testbed for rapid and flexible development of autonomy algorithms pertaining to autonomous driving (using ADSS Toolkit) as well as smart city management (using SCSS Toolkit). It supports local (decentralized) as well as distributed (centralized) computing and is compatible with Robot Operating System (ROS), while also offering a direct scripting support for Python and C++.

Source Branch: AutoDRIVE Devkit
Latest Release: AutoDRIVE Devkit 0.3.0
Upcoming Release: AutoDRIVE Devkit 0.4.0 is currently under development.

Awards and Recognition

Finalist Award for project “Nigel: A Mechatronically Redundant and Reconfigurable Scaled Autonomous Vehicle of AutoDRIVE Ecosystem” at ASME Student Mechanism and Robot Design Competition (SMRDC) 2023
Best Paper Award for paper “AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education” at CCRIS 2021
Best Project Award for “AutoDRIVE – An Integrated Platform for Autonomous Driving Research and Education” at National Level IEEE Project Competition 2021
Best Project Award for “AutoDRIVE – An Integrated Platform for Autonomous Driving Research and Education” at SRMIST Mechatronics Department 2021
Gold Medal for paper “AutoDRIVE – An Integrated Platform for Autonomous Driving Research and Education” at SRMIST Research Day 2021
Lightning Talk of “AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education” at ROS World 2020
India Connect @ NTU Research Fellowship 2020 for “AutoDRIVE Simulator”

Resources

Highlights

We encourage you to take a look at the following quick highlights to keep up with the recent advances in AutoDRIVE Ecosystem.



AutoDRIVE Ecosystem Pitch Video



AutoDRIVE Simulator Pitch Video	AutoDRIVE Testbed Pitch Video

Nigel 4WD4WS Feature Video	F1TENTH in AutoDRIVE Simulator

OpenCAV in AutoDRIVE Simulator	RZR in AutoDRIVE Simulator

Parallel RL using AutoDRIVE Simulator	Deformable Terrain in AutoDRIVE Simulator

Variability Testing using Nigel	Variability Testing using OpenCAV

Demonstrations

We encourage you to take a look at the following research projects developed using the AutoDRIVE Ecosystem.



Autonomous Parking	Behavioural Cloning

Intersection Traversal	Smart City Management

Presentations

We encourage you to take a look at the following presentations to gain a better insight into the AutoDRIVE Ecosystem.



SRMIST UG Final Year Project Viva Voce	CCRIS 2021 Virtual Presentation

AutoDRIVE Technical Discussion @ ARMLab CU-ICAR	Autoware COE Seminar

AIM 2023 Video Presentation	OpenCAV Technical Discussion @ ARMLab CU-ICAR

OpenCAV CUICAR AuE Seminar	SMRDC 2023 Finalist Pitch

MECC 2023 Video Pesentation	IROS 2023 Presentation

Publications

We encourage you to read and cite the following papers if you use any part of this project for your research:

AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education

@article{AutoDRIVE-Ecosystem-2023,
author = {Samak, Tanmay and Samak, Chinmay and Kandhasamy, Sivanathan and Krovi, Venkat and Xie, Ming},
title = {AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research &amp; Education},
journal = {Robotics},
volume = {12},
year = {2023},
number = {3},
article-number = {77},
url = {https://www.mdpi.com/2218-6581/12/3/77},
issn = {2218-6581},
doi = {10.3390/robotics12030077}
}

This work has been published in MDPI Robotics. The open-access publication can be found on MDPI.

AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

@inproceedings{AutoDRIVE-Simulator-2021,
author = {Samak, Tanmay Vilas and Samak, Chinmay Vilas and Xie, Ming},
title = {AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education},
year = {2021},
isbn = {9781450390453},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3483845.3483846},
doi = {10.1145/3483845.3483846},
booktitle = {2021 2nd International Conference on Control, Robotics and Intelligent System},
pages = {1–5},
numpages = {5},
location = {Qingdao, China},
series = {CCRIS'21}
}

This work has been published at 2021 International Conference on Control, Robotics and Intelligent System (CCRIS). The publication can be found on ACM Digital Library.

Towards Mechatronics Approach of System Design, Verification and Validation for Autonomous Vehicles

@inproceedings{AutoDRIVE-Mechatronics-2023,
author = {Samak, Chinmay and Samak, Tanmay and Krovi, Venkat},
booktitle = {2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}, 
title = {Towards Mechatronics Approach of System Design, Verification and Validation for Autonomous Vehicles}, 
year = {2023},
volume = {},
number = {},
pages = {1208-1213},
doi = {10.1109/AIM46323.2023.10196233},
url = {https://doi.org/10.1109/AIM46323.2023.10196233}
}

This work has been published at 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). The publication can be found on IEEE Xplore.

Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem

@eprint{AutoDRIVE-Sim2Real-2023,
title={Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem}, 
author={Chinmay Vilas Samak and Tanmay Vilas Samak and Venkat Krovi},
year={2023},
eprint={2307.13272},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

This work has been accepted at 2023 AACC/IFAC Modeling, Estimation and Control Conference (MECC). The open-access publication can be found on ScienceDirect.

Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem

@eprint{AutoDRIVE-MARL-2023,
title={Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem}, 
author={Tanmay Vilas Samak and Chinmay Vilas Samak and Venkat Krovi},
year={2023},
eprint={2309.10007},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

This work has been accepted as Multi-Agent Dynamic Games (MAD-Games) Workshop paper at 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). The publication can be found on MAD-Games Workshop Website.

Nigel - Mechatronic Design and Robust Sim2Real Control of an Over-Actuated Autonomous Vehicle

@eprint{Nigel-4WD4WS-2024,
title={Nigel -- Mechatronic Design and Robust Sim2Real Control of an Over-Actuated Autonomous Vehicle}, 
author={Chinmay Vilas Samak and Tanmay Vilas Samak and Javad Mohammadpour Velni and Venkat Narayan Krovi},
year={2024},
eprint={2401.11542},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Technical Reports

We encourage you to read and cite the following technical reports if you use any part of this project for your research (these can serve as a good source of documentation as well):

AutoDRIVE - Technical Report

@misc{AutoDRIVE-Technical-Report,
doi = {10.48550/ARXIV.2211.08475},
url = {https://arxiv.org/abs/2211.08475},
author = {Samak, Tanmay Vilas and Samak, Chinmay Vilas},
keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {AutoDRIVE - Technical Report},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}

AutoDRIVE Simulator - Technical Report

@misc{AutoDRIVE-Simulator-Technical-Report,
doi = {10.48550/ARXIV.2211.07022},
url = {https://arxiv.org/abs/2211.07022},
author = {Samak, Tanmay Vilas and Samak, Chinmay Vilas},
keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {AutoDRIVE Simulator - Technical Report},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}