Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks
arxiv(2024)
摘要
The F1TENTH autonomous driving platform, consisting of 1:10-scale
remote-controlled cars, has evolved into a well-established education and
research platform. The many publications and real-world competitions span many
domains, from classical path planning to novel learning-based algorithms.
Consequently, the field is wide and disjointed, hindering direct comparison of
developed methods and making it difficult to assess the state-of-the-art.
Therefore, we aim to unify the field by surveying current approaches,
describing common methods, and providing benchmark results to facilitate clear
comparisons and establish a baseline for future work. This research aims to
survey past and current work with F1TENTH vehicles in the classical and
learning categories and explain the different solution approaches. We describe
particle filter localisation, trajectory optimisation and tracking, model
predictive contouring control, follow-the-gap, and end-to-end reinforcement
learning. We provide an open-source evaluation of benchmark methods and
investigate overlooked factors of control frequency and localisation accuracy
for classical methods as well as reward signal and training map for learning
methods. The evaluation shows that the optimisation and tracking method
achieves the fastest lap times, followed by the online planning approach.
Finally, our work identifies and outlines the relevant research aspects to help
motivate future work in the F1TENTH domain.
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