The F1TENTH autonomous racing platform, consisting of 1:10 scale RC cars, has evolved into a leading 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 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 comparison and establish a baseline for future work. We survey current work in F1TENTH racing in the classical and learning categories, explaining the different solution approaches. We describe particle filter localisation, trajectory optimisation and tracking, model predictive contouring control (MPCC), 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 and 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 MPCC planner. Finally, our work identifies and outlines the relevant research aspects to help motivate future work in the F1TENTH domain.

翻译：F1TENTH自主赛车平台以1:10比例的遥控车为基础，已发展成为领先的研究平台。众多学术论文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，导致方法难以直接比较，评估最新技术成果也颇为困难。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，以促进清晰对比并为未来工作建立基线。本文综述了F1TENTH赛车在经典与学习类别中的现有工作，阐释了不同的解决方案。我们详细介绍了粒子滤波定位、轨迹优化与跟踪、模型预测轮廓控制（MPCC）、间隙跟随算法以及端到端强化学习。我们提供了基准方法的开源评估，并探讨了经典方法中控制频率和定位精度、以及学习方法中奖励信号和训练地图等常被忽视的因素。评估表明，优化与跟踪方法实现了最快圈速，其次是MPCC规划器。最后，我们的工作识别并概述了相关研究要点，以助力F1TENTH领域的未来研究。