  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.
