使用可解释的深度学习和不确定性量化,在无保护的左转过程中建模决策
Yubin Chen1, Yajie Zou1, Jun Liu2
1Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China.
Accident; analysis and prevention
|June 13, 2025
概括
司机在无保护的左转时面临复杂的决策. 这项研究量化了决策不确定性,揭示了更高的不确定性与增加的风险和不安全的机动有关,影响自动驾驶汽车的安全.
科学领域:
- 交通安全 交通安全
- 人与计算机的交互
- 自主系统 自主系统
背景情况:
- 不受保护的左转是复杂的驾驶场景,需要独特的决策.
- 现有的模型往往忽视了信息变化和内在决策机制.
- 了解在不确定性下驾驶员的决策对于道路安全至关重要.
研究的目的:
- 通过决策不确定性的透视来分析驾驶员在不受保护的左转时的决策.
- 探索决策不确定性与驾驶安全之间的关系.
- 确定影响左转决策的关键变量并量化不确定性.
主要方法:
- 冲突区域计算以确定相互作用事件.
- 变压器模型和沙普利添加式解释,以确定关键决策变量.
- 詹森-香农分歧来量化决策不确定性.
主要成果:
- 左转车辆优先考虑静态变量 (等待时间,车辆类型);相对车辆关注动态变量 (停车时间,速度差异).
- 时间压力增加了侧向速度和曲折角度的重视.
- 更高的不确定性与更长的谈判时间,更短的侵占后时间以及紧急制动的可能性增加有关.
结论:
- 决策不确定性是无保护左转安全的一个关键因素.
- 洞察力为自动驾驶汽车的决策框架提供信息,以实现更安全的导航.
- 在无保护的左转中,驾驶员的行为受到静态和动态变量的复杂相互作用的影响.
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