在实时撞车风险预测中评估交通冲突措施的性能,使用撞车前的车辆轨迹
Kequan Chen1, Chengcheng Xu1, Pan Liu1
1School of Transportation, Southeast University, Nanjing, 211189, China.
Accident; analysis and prevention
|May 17, 2024
概括
这项研究评估了使用无人机数据预测后方撞车的交通冲突措施. 时间到碰撞 (TTC) 显示出最佳性能,并确定了早期预警系统的最佳值.
科学领域:
- 交通安全工程 交通安全工程
- 运输系统分析 运输系统分析
- 机器学习用于预测建模.
背景情况:
- 实时撞车风险预测对于交通安全至关重要.
- 传统方法在动态环境中往往缺乏准确性.
- 基于无人机的轨迹数据为分析机前体提供了一种新的方法.
研究的目的:
- 评估交通冲突措施的有效性,以实时预测碰撞风险.
- 为了比较时间到碰撞 (TTC),减速率以避免碰撞 (DRAC) 和即时加速 (ADIA) 导数的绝对值的性能.
- 确定车祸预警系统中实际应用的最佳门.
主要方法:
- 利用来自中国高速公路的无人机记录来捕捉车辆轨迹.
- 应用极端值理论 (EVT) 采用区块最大采样来分析风险事件的通用极端值 (GEV) 分布.
- 将车辆碰撞前的轨迹分成不同的时间窗口,以模拟不同的碰撞条件.
主要成果:
- 预测性能随着崩前的时间窗口增加而下降.
- 碰撞时间 (TTC) 始终超过减速率以避免碰撞 (DRAC) 和即时加速度导数 (ADIA) 的绝对值.
- 对于提前2秒以上的预测,TTC可靠性下降;确定了TTC和ADIA的最佳值.
结论:
- 交通冲突措施,特别是TTC,显示出对实时撞车风险预测的前景.
- 开发的EVT框架为评估事故风险提供了一个强大的方法.
- 研究结果支持将这些方法应用于自动驾驶汽车安全系统和交通管理.
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