一种动态方法来预测在曲线上驾驶风险,使用多源数据来预测
Yongfeng Ma1, Fan Wang1, Shuyan Chen1
1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
Accident; analysis and prevention
|July 23, 2023
概括
使用连接车辆数据预测在曲线上驾驶风险对于安全至关重要. 一个新的LSTM模型有效地识别高风险的驾驶行为,优于其他算法.
科学领域:
- 道路安全工程工程 道路安全工程
- 智能运输系统 智能运输系统
- 在汽车安全领域的机器学习应用.
背景情况:
- 利的曲线带来了显著的交通事故风险,特别是在不利的驾驶条件下.
- 在曲的道路上实时预测风险对于提高互联汽车驾驶员安全至关重要.
- 现有的方法可能无法充分捕捉曲线上驾驶风险的动态性质.
研究的目的:
- 开发一种动态的实时方法,用于预测在曲线上驾驶的风险.
- 合并多个数据源,包括驾驶员机动,车辆动力学和生理数据,以进行全面的风险评估.
- 评估长短期记忆 (LSTM) 网络模型与其他机器学习算法进行风险预测的性能.
主要方法:
- 在六个曲线上进行了一项驾驶模拟实验,曲线的半径和方向各不相同.
- 从55名参与者中收集了驾驶员机动,车辆动力学和驾驶员生理学数据.
- 数据被细分,并使用临界横向加速作为风险指数,使用K-means集群将其分类为低,中和高水平.
- 开发了一个LSTM网络模型,使用合并数据预测风险水平,确定最佳回顾和延迟窗口.
- 将LSTM模型性能与随机森林,XGBoost和LightGBM算法进行了比较.
主要成果:
- 提出的基于LSTM的模型在预测在曲线上危险的驾驶行为方面表现出了卓越的表现.
- 对于LSTM模型来说,最优的窗口组合被确定为20米的回顾和20米的延迟.
- 在LSTM模型中,中等风险的F1得分为84.8%,高风险的F1得分为86.0%,超过了比较算法.
- 多源数据融合方法显著优于仅使用车辆动力学数据的模型.
结论:
- 使用LSTM开发的动态实时风险预测方法在联网车辆环境中对曲线有效.
- 通过利用合并的多源数据,LSTM模型为预测驾驶风险提供了强大的解决方案.
- 这种方法可以为智能互联汽车的实时预测和预警系统的开发做出重大贡献.
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