一种基于在线强化学习的冲突解决方法,用于人车合作系统
Viet Thang Dang1, Duy Lam Mac1, Duc Thinh Le2
1Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, 10000, Vietnam.
ISA transactions
|December 18, 2025
概括
本研究介绍了合作驾驶系统的新型冲突解决方法. 它通过使用强化学习来最大限度地减少轨迹错误和驾驶员自动化冲突来提高安全性和人类体验.
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
- 控制系统 控制系统
背景情况:
- 在合作驾驶系统中,司机与自动化之间的冲突带来了重大挑战.
- 这些冲突可能会对人类体验,信任和整体驾驶安全产生负面影响.
研究的目的:
- 为人类-车辆合作系统提出一种新的冲突解决方法.
- 为了最大限度地减少轨迹跟踪错误,并减轻人类驾驶员和自动化之间的冲突.
主要方法:
- 引入了车辆-驾驶员冲突模型,结合了驾驶员在循环中的视角.
- 开发了一个在线增强学习算法来计算自动化方向盘角度.
- 利用Lyapunov的理论来确保模型稳定性和控制趋同.
主要成果:
- 拟议的方法有效地减少了轨迹跟踪中的错误.
- 强化学习算法成功计算自动化转向角度,即使在冲突场景.
- 利亚普诺夫的理论保证了系统状态的统一的最终界限性和控制信号的融合.
结论:
- 开发的方法通过解决驾驶员-自动化冲突来增强合作驾驶系统.
- 该方法改善了轨迹跟踪,并通过先进的控制技术确保了稳定性.
- 数字模拟验证了与现有方法相比,建议方法的有效性和优越性.
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