Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks

TL;DR

This paper surveys F1TENTH autonomous racing approaches, compares methods through benchmarks, and provides insights to unify the field and guide future research in classical and learning-based algorithms.

Contribution

It offers a comprehensive survey, benchmark evaluation, and analysis of classical and learning methods for F1TENTH autonomous racing, establishing a baseline for future work.

Findings

Optimization and tracking achieve the fastest lap times.

Online planning approaches perform well but slower.

Control frequency and localisation impact performance.

Abstract

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.