改进了自动驾驶汽车的观察者设计方法,使用基于错误的超局部模型
Daniel Fenyes1, Tamas Hegedus2, Balazs Nemeth2,3
1HUN-REN Institute for Computer Science and Control (SZTAKI), Kende u. 13-17, 1111, Budapest, Hungary. daniel.fenyes@sztaki.hu.
Scientific reports
|July 11, 2025
概括
这项研究引入了自动驾驶汽车的新观察者设计,将线性参数变化 (LPV) 方法与基于错误的超局部模型相结合,以提高车辆动态的估计精度.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 汽车工程 汽车工程
背景情况:
- 自动驾驶汽车系统需要准确的状态估计,以确保安全高效的运行.
- 传统的观察者设计经常与车辆动态固有的模型不确定性和非线性作斗争.
- 现有的方法可能无法充分解决自动驾驶汽车遇到的复杂现实条件.
研究的目的:
- 开发一种用于自动驾驶汽车状态估计的新型观察者设计方法.
- 通过整合基于错误的超局部模型来提高线性参数变化 (LPV) 观察者的性能.
- 为了有效地管理车辆模型中的非模拟动态和非线性.
主要方法:
- 将线性参数变化 (LPV) 框架与基于错误的超局部模型相结合.
- 基于错误的超局部模型被用来弥补不确定性和非线性.
- 实现自动驾驶汽车侧向速度的具体估计.
主要成果:
- 由于新设计,LPV观测器的性能得到了显著改善.
- 通过模拟验证观察算法的效率和操作能力.
- 使用ZalaZone试验场的现实世界测试测量来证明有效性.
结论:
- 拟议的观察员设计方法有效地提高了对自动驾驶汽车的状态估计.
- 集成LPV和基于错误的超局部模型为处理模型不确定性提供了强大的解决方案.
- 该方法显示了实际可用性,通过模拟和真实世界数据得到证实.
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