TGLD:一种基于游戏理论的信任意识的车道改变决策框架,用于在异质交通中使用自动驾驶汽车
Jie Pan1, Yongjun Shen2, Chengyu He1
1Department of Civil Engineering, Tsinghua University, Beijing, China.
Accident; analysis and prevention
|December 25, 2025
概括
自动驾驶汽车 (AV) 现在可以根据人类驾驶员的信任来适应车道更改策略. 这种信任意识的框架增强了与人驾驶汽车 (HV) 在混合交通环境中的合作和安全.
科学领域:
- 智能运输系统 智能运输系统
- 机器人和控制机器人技术
- 人与计算机的交互
背景情况:
- 自动驾驶汽车 (AVs) 需要社会兼容的行为来安全地与人驾驶汽车 (HVs) 集成.
- 现有的AV决策框架往往忽视了HV的动态信任水平,限制了混合流量的准确行为预测.
- 人类驾驶员行为的不确定性在异质的交通环境中对自动驾驶汽车构成重大挑战.
研究的目的:
- 为AV改变车道的决定提出一种新的信任意识,游戏理论框架.
- 为了提高社会兼容性和AV行为在混合交通中的可预测性.
- 开发一个基于实时人类驾驶员信任的动态调整自动驾驶策略的系统.
主要方法:
- 开发了一款基于信任的多车辆联盟游戏,集成合作型自动驾驶和部分合作型自动驾驶.
- 设计了一种在线信任评估方法,以动态估计车道交换交互期间的HV信任.
- 在高速公路上坡道场景中进行了人身循环实验,以验证框架.
主要成果:
- 该AV成功地调整了其车道更改策略,以适应不同的人类驾驶风格和信任水平.
- 拟议的模型促进了安全,高效和舒适的AV合并行为.
- 废弃研究表明,信任机制改善了平均变车道速度,提高了效率而不会损害安全.
结论:
- 信任意识框架提高了自动驾驶汽车与人类驾驶员安全高效互动的能力.
- 这项研究促进了自动驾驶系统更透明,更适应性更改车道的策略.
- 提高人车互动的可解释性对于未来的自动驾驶开发至关重要.
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