一个动态风险预测框架的车道变化行为,基于驾驶意图识别在冰和雪地表面
Wei Zhao1, Xuejing Du2, Zhanyu Wang2
1School of Mechanical Engineering, Shandong University, Jinan, 250100, China.
Scientific reports
|March 3, 2026
概括
这项研究引入了一个新的框架,通过结合驾驶意图识别来预测车辆在冰的道路上改变车道的风险. 先进的模型实现了高精度,提高了智能汽车在危险条件下的安全性.
科学领域:
- 道路安全和智能交通运输系统
- 机器学习在汽车工程中的应用.
- 环境对驾驶行为的影响
背景情况:
- 改变车道的事故在冰和积雪的表面上很常见.
- 现有的风险预测模型缺乏驾驶意图识别,限制了准确性.
- 对于危险的驾驶条件需要先进的预测框架.
研究的目的:
- 提出一个动态的车道更改风险预测框架,用于冰雪/雪地表面.
- 将驾驶意图识别纳入车道更换风险评估.
- 提高风险预测模型的准确性和实际适用性.
主要方法:
- 利用驾驶模拟数据用于冰雪和雪地条件.
- 使用双向多层长期短期记忆 (Multi-BiLSTM) 进行意图识别.
- 应用故障树分析,车道变化风险指数,k-means集群和光梯度增强机 (LightGBM) 进行风险预测.
主要成果:
- 改变车道的意图持续时间在冰/雪面上 (6.12s) 增加了36.3%.
- 多BiLSTM实现了高精度 (98.22%左, 97.74%右, 96.31%保持).
- 轻GBM模型预测了97.49%的整体准确性改变车道的风险,超过了其他算法.
结论:
- 拟议的框架有效地预测了危险表面的车道更改风险.
- 驾驶意图识别显著提高了预测准确度.
- 这些发现支持在恶劣天气下为智能车辆开发风险预警系统.
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