F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:77-89, 2020.
The deployment and evaluation of learning algorithms on autonomous vehicles (AV) is expensive, slow, and potentially unsafe. This paper details the F1TENTH autonomous racing platform, an open-source evaluation framework for training, testing, and evaluating autonomous systems. With 1/10th-scale low-cost hardware and multiple virtual environments, F1TENTH enables safe and rapid experimentation of AV algorithms even in laboratory research settings. We present three benchmark tasks and baselines in the setting of autonomous racing, demonstrating the flexibility and features of our evaluation environment.