量化驾驶感知风险:避免障碍的蒙特卡洛方法
Zhen Yang1, Zhe Gong1, Yimei Qin1
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China.
Traffic injury prevention
|October 17, 2024
概括
这项研究使用蒙特卡洛和logit模型量化了驾驶员在避开障碍时感知到的风险. 调查结果显示,感知到的风险和速度显著影响绕道决策,改善了交通安全预测.
科学领域:
- 交通安全 交通安全
- 驾驶过程中的人类因素
- 风险感知建模风险感知建模
背景情况:
- 从司机的角度来看,有限的研究模型驱动风险.
- 了解驾驶员的风险感知对于提高道路安全至关重要.
研究的目的:
- 开发一种定量模型,用于避免障碍的感知风险.
- 调查司机的风险感知如何影响他们的驾驶决策.
- 将模糊的风险感知纳入概率驾驶模型.
主要方法:
- 使用蒙特卡洛方法来模拟驾驶不确定性.
- 使用司机模糊的感知量化感知风险.
- 使用logit模型将感知到的风险与驾驶决策联系起来.
主要成果:
- 实验数据显示,根据驾驶员的经验,车辆轨迹的变化.
- 对左侧绕道的感知风险指标 (PRI) 值较高.
- PRI显示出强大的预测能力 (AUC=0.820);logit模型在预测右转绕道时达到90%的准确性.
结论:
- 该研究为了解驾驶员的风险感知和决策提供了一个框架.
- 这些发现有助于交通专业人员提高交通安全.
- 强调驾驶者在驾驶行为模型中的风险感知的重要性.
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