Design of an Autonomous Racecar: Perception, State Estimation and System Integration

TL;DR

This paper details the design and implementation of fl"uela driverless, an autonomous racecar that successfully completed a competitive race using onboard sensors, robust system integration, and advanced perception and estimation techniques, outperforming competitors.

Contribution

It introduces a modular, robust autonomous racecar system with integrated perception, state estimation, and SLAM, achieving competitive lap times in a real-world racing environment.

Findings

Outperformed the next-best team by nearly double the speed.

Achieved human-expert lateral and longitudinal accelerations.

Demonstrated robustness under challenging perceptual conditions.

Abstract

This paper introduces fl\"uela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of fl\"uela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.