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.
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| 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
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| 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.
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| 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.
- 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"
We encourage you to take a look at the following quick highlights to keep up with the recent advances in AutoDRIVE Ecosystem.
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| AutoDRIVE Ecosystem Pitch Video |
We encourage you to take a look at the following research projects developed using the AutoDRIVE Ecosystem.
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| Autonomous Parking | Behavioural Cloning |
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| Intersection Traversal | Smart City Management |
We encourage you to take a look at the following presentations to gain a better insight into the AutoDRIVE Ecosystem.
We encourage you to read and cite the relevant 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 & 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.
@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.
@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.
@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.
@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}
}This work has been accepted in IEEE/ASME Transactions on Mechatronics (TMECH) and additionally accepted to be presented at 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). The publication can be found on IEEE Xplore.
@eprint{AutoDRIVE-Autoware-2024,
title = {Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework},
author = {Tanmay Vilas Samak and Chinmay Vilas Samak and Venkat Narayan Krovi},
year = {2024},
eprint = {2402.12670},
archivePrefix = {arXiv},
primaryClass = {cs.RO}
}This work has been accepted at 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).
A Scalable and Parallelizable Digital Twin Framework for Sustainable Sim2Real Transition of Multi-Agent Reinforcement Learning Systems
@eprint{AutoDRIVE-DT-MARL-2024,
title = {A Scalable and Parallelizable Digital Twin Framework for Sustainable Sim2Real Transition of Multi-Agent Reinforcement Learning Systems},
author = {Chinmay Vilas Samak and Tanmay Vilas Samak and Venkat Krovi},
year = {2024},
eprint = {2403.10996},
archivePrefix = {arXiv},
primaryClass = {cs.RO}
}Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters
@eprint{AutoDRIVE-HPC-RZR-2024,
title = {Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters},
author = {Tanmay Vilas Samak and Chinmay Vilas Samak and Joey Binz and Jonathon Smereka and Mark Brudnak and David Gorsich and Feng Luo and Venkat Krovi},
year = {2024},
eprint = {2405.04743},
archivePrefix = {arXiv},
primaryClass = {cs.RO}
}This work has been accepted at 2024 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS). Distribution Statement A. Approved for public release; distribution is unlimited. OPSEC #8451.
We encourage you to read and cite the relevant technical reports if you use any part of this project for your research (these can serve as a good source of documentation as well):
@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}
}@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}
}@misc{AutoDRIVE-Autoware-Technical-Report,
doi = {10.48550/arXiv.2402.14739},
url = {https://doi.org/10.48550/arXiv.2402.14739},
author = {Chinmay Vilas Samak and Tanmay Vilas Samak},
title = {Autonomy Oriented Digital Twins for Real2Sim2Real Autoware Deployment},
year = {2024},
eprint = {2402.14739},
archivePrefix = {arXiv},
primaryClass = {cs.RO}
copyright = {arXiv.org perpetual, non-exclusive license}
} |
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| Tanmay Vilas Samak | Chinmay Vilas Samak |
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| Rohit Ravikumar | Parth Shinde | Joey Binz | Giovanni Martino |
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| Dr. Venkat Krovi | Dr. Sivanathan Kandhasamy | Dr. Ming Xie |
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| CU-ICAR | SRM-IST | NTU |
