基于交通环境和驾驶风格的车道更换风险预测模型
Yingwei Ren1, Fangzheng Li2, Zihe Li1
1College of Transportation, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|October 8, 2025
概括
这项研究引入了一种先进的车道更换风险预测模型. 整合交通环境和驾驶风格显著提高了对更安全驾驶的预测准确性.
科学领域:
- 道路交通安全问题 道路安全问题
- 交通运输中的人工智能
- 车辆动力学 车辆动力学
背景情况:
- 现有的变车道风险模型在复杂的交通中缺乏适应性.
- 交通环境和驾驶风格是风险预测中未得到充分利用的因素.
研究的目的:
- 开发一个集成的车道更换风险预测模型.
- 通过考虑交通环境和驾驶风格来提高驾驶安全.
- 提高车道更换风险评估的准确性和适应性.
主要方法:
- 注意-LSTM用于交通背景和车道更改类型识别.
- 用于驾驶风格的表征的LSTM-堆叠的Denoising自动编码器 (LSTM-SDAE).
- 轻度梯度增强机 (LGBM) 用于风险预测,利用Shapley增量解释 (SHAP) 来确定特征的重要性.
主要成果:
- 随着交通背景集成,模型准确性从88.79%提高到95.05%.
- 具有集成功能的LGBM模型优于其他算法.
- 对于左侧与右侧车道变化,特征的重要性有所不同,突出侧向和纵向速度和驾驶风格.
结论:
- 整合交通环境和驾驶风格显著提高了变车道风险预测的准确性.
- 拟议的模型为改善驾驶安全系统提供了有价值的见解.
- 这种方法为复杂的交通场景中更强大的决策支持系统提供了基础.
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