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.
翻译:本文介绍了2018年“Smart Shark II”的自主系统,它赢得了“公式学生自治中国(FSAC)”竞争。在这一竞争中,需要一辆自主的赛车来自动完成两圈未知轨道。在本文中,作者介绍了该赛车的自驾驶软件结构,确保车辆高速和安全。主要部件确保了赛车的稳定驾驶,基于LiDAR和基于愿景的锥形探测提供了一种冗余感;基于EKF的本地化提供了高精确度和高频率的国家估计;认知结果通过占用网图在时间和空间上积累。在获得轨迹后,模型预测控制算法被用于优化对赛车的纵向和横向控制。最后,展示了基于现实世界数据的实验的绩效。