基于预测位置的时空重叠概率的高速公路车辆的统一风险指标
Chuang Cui1, Bocheng An1, Linheng Li1
1School of Transportation, Southeast University, Nanjing, Jiangsu Province 211189, China.
Accident; analysis and prevention
|April 22, 2025
概括
基于预测位置 (RMPP) 的新风险指标提供了一个统一的框架来量化驾驶风险. 这种先进的指标通过考虑未来的车辆位置和不确定性来改善交通安全管理.
科学领域:
- 交通安全和智能运输系统
- 机器学习用于预测建模.
- 计算风险评估计算风险评估
背景情况:
- 现有的替代安全措施 (SSM) 往往专注于有限的场景,并假设确定性的车辆运动.
- 当前的指标忽视了现实世界的车辆运行中固有的不确定性,限制了它们的积极安全管理能力.
- 需要一个全面的风险评估工具来考虑未来的车辆位置和运行不确定性.
研究的目的:
- 引入基于预测位置 (RMPP) 的新型风险指标,以统一全面量化驾驶风险.
- 开发一种包含未来车辆位置分布的方法来评估碰撞概率.
- 为了验证RMPP的有效性和稳定性与传统指标在不同的驾驶场景.
主要方法:
- 使用通用动态图卷积网络 (GDGCN) 来预测图形结构中的车辆的未来位置分布.
- 计算空间近距离风险 (位置在同一时间重叠的概率) 和时间近距离风险 (不同时间重叠的概率).
- 通过加权总和来得出RMPP的综合空间和时间风险,使用CitySim数据集进行验证.
主要成果:
- 与基线模型相比,GDGCN模型证明了车辆位置的高预测准确性.
- 在现实条件下,RMPP有效地比传统指标 (TTC,DRAC,SM) 更准确地捕获了驾驶风险特征.
- 一旦达到一定准确度值,RMPP显示出可靠性和稳定性,预测准确度变化的影响最小.
结论:
- 拟议的RMPP通过整合预测未来车辆位置,为驾驶风险评估提供了强大而全面的方法.
- 与传统指标相比,RMPP提供了显著的进步,更好地与现实世界的驾驶复杂性和不确定性保持一致.
- 随着预测技术的改进,RMPP有望成为主动交通安全管理的有价值工具,并有可能提高精度.
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