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.

翻译：F1TENTH自动驾驶平台由1:10比例遥控赛车构成，已发展成为成熟的教育与科研平台。众多出版物和真实世界竞赛涵盖从经典路径规划到新型学习算法的多个领域。然而，该领域范围广泛且分散，阻碍了所开发方法的直接比较，使评估最新技术变得困难。为此，我们旨在通过综述现有方法、描述通用技术并提供基准测试结果来统一该领域，从而促进清晰比较并为未来研究建立基线。本研究系统梳理了F1TENTH车辆在经典与学习类别中的过往及当前工作，阐释了不同解决方案的技术路径。我们详细描述了粒子滤波定位、轨迹优化与跟踪、模型预测轮廓控制、间隙跟踪以及端到端强化学习。通过开源基准方法评估，我们探究了控制频率与定位精度对经典方法的影响，以及奖励信号与训练地图对学习方法的影响等被忽视因素。评估表明，优化与跟踪方法实现了最快圈速，其次是在线规划方法。最后，本文识别并概述了相关研究要点，旨在为F1TENTH领域的未来工作提供动力。