Advancing Autonomous Racing: A Comprehensive Survey of the RoboRacer (F1TENTH) Platform

TL;DR

This survey reviews the RoboRacer (F1TENTH) platform's hardware, software, and research applications, emphasizing its role in advancing autonomous driving research, education, and bridging the simulation-to-reality gap.

Contribution

It provides a comprehensive overview of RoboRacer's architecture, applications, and advancements, highlighting its importance in autonomous systems research and education.

Findings

RoboRacer facilitates scalable autonomous racing research

The platform supports diverse perception, planning, and control algorithms

It plays a key role in bridging the simulation-to-reality gap

Abstract

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.