AutoDRIVE -- Technical Report
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Abstract (arXiv)
This work presents AutoDRIVE, a comprehensive research and education platform for implementing and validating intelligent transportation algorithms pertaining to vehicular autonomy as well as smart city management. It is an openly accessible platform featuring a 1:14 scale car with realistic drive and steering actuators, redundant sensing modalities, high-performance computational resources, and standard vehicular lighting system. Additionally, the platform also offers a range of modules for rapid design and development of the infrastructure. The AutoDRIVE platform encompasses Devkit, Simulator and Testbed, a harmonious trio to develop, simulate and deploy autonomy algorithms. It is compatible with a variety of software development packages, and supports single as well as multi-agent paradigms through local and distributed computing. AutoDRIVE is a product-level implementation, with a vast scope for commercialization. This versatile platform has numerous applications, and they are bound to keep increasing as new features are added. This work demonstrates four such applications including autonomous parking, behavioural cloning, intersection traversal and smart city management, each exploiting distinct features of the platform.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Nov 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Educationcited by 10
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Frameworkcited by 1
- 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystemcited by 0
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework20 Feb 2024
- 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem18 Sep 2023
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education10 Dec 2022
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- 2023Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem
- 2025Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions
- 2017CARLA: An Open Urban Driving Simulator
- 2025AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software
- 2025A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences
- 2024AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning
- 2021Autonomy 2.0: Why is self-driving always 5 years away?
- 2024Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System