AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education
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Abstract (arXiv)
AutoDRIVE is envisioned to be an integrated research and education platform for scaled autonomous vehicles and related applications. This work is a stepping-stone towards achieving the greater goal of realizing such a platform. Particularly, this work introduces the AutoDRIVE Simulator, a high-fidelity simulator for scaled autonomous vehicles. The proposed simulation ecosystem is developed atop the Unity game engine, and exploits its features in order to simulate realistic system dynamics and render photorealistic graphics. It comprises of a scaled vehicle model 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 also provides a modular environment development kit, which comprises of various environment modules that aid in reconfigurable construction of the scene. Additionally, the simulator features a communication bridge in order to extend an interface to the autonomous driving software stack developed independently by the users. This work describes some of the prominent components of this simulation system along with some key features that it has to offer in order to accelerate education and research aimed at autonomous driving.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1.
5 papers.
- 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Educationcited by 10
- 2025When Digital Twins Meet Large Language Models: Realistic, Interactive, and Editable Simulation for Autonomous Drivingcited by 3
- 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
- 2025When Digital Twins Meet Large Language Models: Realistic, Interactive, and Editable Simulation for Autonomous Driving30 Jun 2025
- 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
- 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles20 Jun 2022