通过整合多个指标进行个性化驾驶风险评估和滚动预测方法
Yuran Li1, Guizhen Chen1, Yikai Luo1
1School of Transportation Engineering, Chang'an University, Xi'an, Shaanxi 710064, China.
Accident; analysis and prevention
|August 21, 2025
概括
这项研究引入了个性化驾驶风险评估方法,使用车辆动态和心率变量 (HRV) 来预测事故风险. 开发的模型达到92.03%的准确性, 优于现有的交通安全方法.
科学领域:
- 交通安全和智能交通系统
- 生物医学工程与人类因素
背景情况:
- 驾驶风格差异很大, 需要进行个性化风险评估, 以确保交通安全.
- 现有系统缺乏基于整合生理和车辆数据的实时个性化事故风险预测.
- 智能驾驶辅助系统需要准确的风险预测才能进行有效的干预.
研究的目的:
- 提出个性化的驾驶风险评估和滚动预测方法.
- 整合车辆动态和心率变量 (HRV) 以实时预测事故风险.
- 开发一个持续驾驶风险预测的先进模型.
主要方法:
- 一个自然驾驶实验收集了关键事件 (CIEs) 和指标数据.
- 一个改进的贝叶斯网络确定了影响CIEs的关键个性化指标.
- 基于贝叶斯优化 (BO) 的双向封闭循环单元 (BiGRU) 与CNN和EKF (BCBGE模型) 集成,用于预测.
主要成果:
- 驾驶风险被分为四个不同级别.
- 个性化分析显示了每种CIE类型的3-4个关键车辆动态或HRV指标.
- BCBGE模型实现了92.03%的预测准确度,超过其他模型的3.37%-15.88%.
结论:
- 建议的个性化方法有效地评估和预测实时的驾驶风险.
- 车辆动态和HRV的整合为智能驾驶员辅助提供了坚实的基础.
- 在连续驾驶风险预测方面,BCBGE模型表现出卓越的性能.
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