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自动驾驶汽车的时间最佳轨迹规划和跟踪
Jun-Ting Li1, Chih-Keng Chen1, Hongbin Ren2
1Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10604, Taiwan.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了自动驾驶汽车的层次控制框架,使得在高速机动时能够精确追踪轨迹. 该系统实现实时性能,以实现最佳的赛道线路跟踪.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 自动驾驶自动驾驶的自动驾驶
背景情况:
- 准确的轨迹跟踪对于自动驾驶汽车至关重要,尤其是在高速动态机动时.
- 现有的方法往往难以平衡轨迹优化与实时跟踪性能.
研究的目的:
- 为自动驾驶汽车轨迹规划和跟踪开发一个层次控制框架.
- 为了在激进的驾驶场景中准确跟踪时间最佳轨迹.
主要方法:
- 一个层次框架,结合了线下轨迹优化 (TRO) 和在线非线性模型预测控制 (NMPC).
- TRO使用直接拼接来生成最小圈时间轨迹.
- 带有预览算法的NMPC确保了实时跟踪准确性.
主要成果:
- 该框架成功地追踪了加泰罗尼亚赛道上平均时速为116公里/小时的最佳时间赛道.
- 最大的横向误差仅限于0.32米.
- 该NMPC模块通过使用具有RTI方案的acados解决器实现了毫秒级计算时间.
结论:
- 拟议的层次控制框架有效地解决了自动驾驶汽车轨迹规划和跟踪挑战.
- 由于高效的NMPC计算,实时实现是可行的.
- 这种方法提高了自动驾驶汽车在动态驾驶条件下的性能.
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