在高风险场景中理解驾驶员认知和决策行为:漂移扩散视角
Heye Huang1, Zheng Li1, Hao Cheng2
1Department of Civil and Environmental Engineering, University of Wisconsin-Madison, WI 53706, USA.
Accident; analysis and prevention
|June 8, 2025
概括
这项研究模拟了混合交通中的人类驾驶员行为,使用一种新的框架来预测高风险情况下的反应. 漂移扩散模型 (DDM) 通过个性化决策来提高自动驾驶汽车 (AV) 的安全性.
科学领域:
- 自主系统 自主系统
- 人与计算机的交互
- 交通安全 交通安全
背景情况:
- 使用自动驾驶汽车 (AV) 和人类驾驶员的混合交通系统带来了安全挑战,特别是在复杂的高风险场景中.
- 了解和建模人类驾驶员的行为,包括风险认知和决策,对于安全的自动驾驶集成至关重要.
研究的目的:
- 开发一个认知决策框架,整合个体驱动器的变化和共同点,以建模风险认知和动态决策.
- 在混合交通环境中提高人机交互的安全性.
主要方法:
- 开发了一个风险敏感性模型,使用多变量高斯分布来描述个人的风险认知.
- 引入了基于漂移扩散模型 (DDM) 的认知决策模型,以捕捉共同的决策机制.
- 在驾驶模拟器中模拟高风险场景 (横向,纵向,多维),以测试模型的预测准确性.
主要成果:
- 拟议的DDM框架准确地预测了在紧急动作期间的认知反应和决策行为.
- 整合驾驶员特定的风险敏感性允许动态调整DDM参数,从而实现个性化决策表示.
- 对比分析显示,DDM比现有模型 (IDM,Gipps,MOBIL) 更准确地捕捉人类的认知过程和适应性决策.
结论:
- 基于DDM的框架提供了一个强大的方法,用于在高风险场景中建模人类驾驶行为.
- 结果为开发更安全的自动驾驶汽车和改善现实交通中的自动驾驶汽车与人类互动提供了关键的见解.
- 这项研究为个性化AV决策算法奠定了理论基础.
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