Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car

2023/08/24 by Aakaash Salvaji, Salvaji, Aakaash, H. Rosemary Taylor +7
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robotics (cs.RO) #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2308.13088

openalex publication_date 2023/08/24 · openalex created_date 2023/08/29 · openalex updated_date 2026/07/28

Abstract

With the rising popularity of autonomous navigation research, Formula Student (FS) events are introducing a Driverless Vehicle (DV) category to their event list. This paper presents the initial investigation into utilising Deep Reinforcement Learning (RL) for end-to-end control of an autonomous FS race car for these competitions. We train two state-of-the-art RL algorithms in simulation on tracks analogous to the full-scale design on a Turtlebot2 platform. The results demonstrate that our approach can successfully learn to race in simulation and then transfer to a real-world racetrack on the physical platform. Finally, we provide insights into the limitations of the presented approach and guidance into the future directions for applying RL toward full-scale autonomous FS racing.

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