From perception to control: an autonomous driving system for a formula student driverless car
This work addresses the specific challenge of autonomous racing for student competitions, representing an incremental improvement in applying existing methods to a real-world racing scenario.
The paper tackles the problem of autonomous driving for a Formula Student racecar by presenting a software structure that integrates perception, localization, and control to ensure high speed and safety, resulting in winning the 2018 FSAC competition with the car completing two laps of an unknown track.
This paper introduces the autonomous system of the "Smart Shark II" which won the Formula Student Autonomous China (FSAC) Competition in 2018. In this competition, an autonomous racecar is required to complete autonomously two laps of unknown track. In this paper, the author presents the self-driving software structure of this racecar which ensure high vehicle speed and safety. The key components ensure a stable driving of the racecar, LiDAR-based and Vision-based cone detection provide a redundant perception; the EKF-based localization offers high accuracy and high frequency state estimation; perception results are accumulated in time and space by occupancy grid map. After getting the trajectory, a model predictive control algorithm is used to optimize in both longitudinal and lateral control of the racecar. Finally, the performance of an experiment based on real-world data is shown.