Design of an Autonomous Racecar: Perception, State Estimation and System Integration
This addresses the problem of robust autonomous racing for competitions, representing a domain-specific incremental advancement.
The paper tackles the challenge of designing an autonomous racecar to win a Formula Student Driverless competition by completing 10 laps on an unknown track using onboard sensing, achieving a result where it outperformed the next-best team by almost half the time per lap and matched accelerations of human drivers.
This paper introduces flüela 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 flüela'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.