综述F1TENTH平台如何推动自动驾驶研究与教育
Advancing Autonomous Racing: A Comprehensive Survey of the RoboRacer (F1TENTH) Platform
- 系统梳理硬件软件架构与研究应用
- 分析仿真到现实的迁移挑战与数据标准
- 适合机器人、自动驾驶方向研究者参考
RoboRacer（F1TENTH）平台已成为推进自动驾驶研究的重要实验平台，提供可扩展、低成本且社区驱动的实验环境。本文全面综述该平台，分析其模块化软硬件架构、多样化的研究应用，以及在自主系统教育中的作用。重点探讨仿真到现实（Sim2Real）的鸿沟、与仿真环境的集成，以及标准化数据集与基准测试的可用性。此外，综述涵盖感知、规划与控制算法的进展，以及全球竞赛和协作研究的成果。通过整合这些贡献，本研究将RoboRacer定位为加速创新、弥合理论研究与实际部署差距的多功能框架。研究结果凸显了该平台在推动自动驾驶赛车与机器人技术发展中的关键作用。
原文摘要 · Abstract (English)
The RoboRacer (F1TENTH) platform has emerged as a leading testbed for advancing autonomous driving research, offering a scalable, cost-effective, and community-driven environment for experimentation. This paper presents a comprehensive survey of the platform, analyzing its modular hardware and software architecture, diverse research applications, and role in autonomous systems education. We examine critical aspects such as bridging the simulation-to-reality (Sim2Real) gap, integration with simulation environments, and the availability of standardized datasets and benchmarks. Furthermore, the survey highlights advancements in perception, planning, and control algorithms, as well as insights from global competitions and collaborative research efforts. By consolidating these contributions, this study positions RoboRacer as a versatile framework for accelerating innovation and bridging the gap between theoretical research and real-world deployment. The findings underscore the platform's significance in driving forward developments in autonomous racing and robotics.
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