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Updated: Mar 3, 2026

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Operation of the Collaborative Composite Manufacturing CCM System
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人机共享驾驶,以避免车辆碰撞,基于汉密尔顿-雅各比可达性.
Shiyue Zhao1, Junzhi Zhang2, Rui Zhou3
1School of Vehicle and Mobility, Tsinghua University, Beijing 10084, China; Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48105, USA.
Accident; analysis and prevention
|March 1, 2026
概括
这项研究引入了一个共享避免碰撞的新框架,使用可达性分析来确保机器只在必要时进行干预. 这种方法可以防止不可避免的碰撞,同时尽量减少对驾驶员的干扰.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 目前用于避免碰撞的共享控制系统往往会破坏驾驶员的意图.
- 现有的方法可以增加人机冲突的风险.
- 需要智能系统,以最小和有效的方式进行干预.
研究的目的:
- 开发一个共享的控制框架,帮助驾驶员在关键的碰撞场景.
- 在自主干预过程中尽量减少人机冲突.
- 确保避免碰撞,只在理论上不可避免的情况下进行干预.
主要方法:
- 利用汉密尔顿 - 雅各比 (HJ) 可达性分析来定义一个避免碰撞可达性集 (CARS).
- 使用线下数据预先计算可访问性分布和CARS.
- 开发了一种驱动模型和权限分配策略,以减少冲突.
- 培训了一个强化学习 (RL) 代理人来执行CARS约束并最大限度地减少冲突.
主要成果:
- 可访问性意识的RL框架在CARS边界之前有效地干预,防止碰撞.
- 该系统在维持原始驾驶任务时表现出更好的性能.
- 稳定性分析证实了不同驾驶员属性的灵活性.
结论:
- 拟议的HJ可访问性引导的RL框架提供了有效的,最少侵入性的共享碰撞避免.
- 这种方法通过防止进入不可避免的碰撞状态来提高安全性.
- 这种方法对现实世界的车辆平台和多样化的驾驶行为有希望.
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