司机如何看待碰撞风险? 司机如何看待碰撞风险? 在概括的二维场景中进行定量探索
Jinghua Wang1, Guangquan Lu2, Wenmin Long1
1School of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Accident; analysis and prevention
|December 13, 2024
概括
本研究引入了二维安全边缘 (TSM) 来量化驾驶感知风险,改进了通用驾驶行为模型和自动驾驶系统. TSM提供了一个场景独立的驾驶员风险感知测量方法.
科学领域:
- 交通动态和自动驾驶系统.
- 人与计算机的互动和行为心理学.
背景情况:
- 现有的驾驶行为模型与动态场景和通用运动作斗争.
- 风险平衡理论 (RHT) 为理解风险行为提供了一个框架,但量化感知风险是具有挑战性的.
- 目前在驾驶过程中使用RHT的应用是使用一维指标,缺乏针对复杂场景的概括风险感知措施.
研究的目的:
- 提出一种新的,二维的安全边际 (TSM),用于量化一般驾驶场景中的感知风险.
- 通过提供更全面的风险指标来增强驾驶行为的建模.
- 通过准确地表示驾驶员的风险感知来支持自动驾驶系统的开发和测试.
主要方法:
- 从驾驶员的角度考虑避免碰撞,开发了二维安全边缘 (TSM).
- 应用TSM以在一般化的二维驾驶场景中建模汽车追踪行为.
- 将TSM的业绩与现有的单维风险指标进行了比较.
主要成果:
- 与现有指标相比,TSM在汽车追踪行为建模的相关性中显示出9.1%的改善.
- 在冲突情况下,TSM准确地捕捉了司机在风险感知方面的个人差异.
- TSM表现出风险稳态,显示正常分布的目标水平,并实现场景独立的风险量化 (平均目标水平为0.85).
结论:
- 二维安全边际 (TSM) 是一个可靠的衡量驾驶员在复杂的二维驾驶场景中的风险感知.
- TSM提高了驾驶行为模型的准确性和通用性,这对于自动驾驶发展至关重要.
- TSM为自动驾驶系统的决策,测试和风险评估提供了一个基本的工具.
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