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

This paper introduces jluela 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 fluela'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.