使用驾驶环境特征在高海拔高速公路上预测驾驶压力:西藏的一个自然驾驶研究
Pengcheng Qin1, Jie He1, Shuang Sun2
1School of Transportation, Southeast University, Nanjing, China.
Traffic injury prevention
|February 16, 2024
概括
在高海拔地区的驾驶压力可以使用环境元素来预测. 机器学习模型根据道路状况,车辆类型和地形准确识别了压力水平,提供了更安全的驾驶评估.
科学领域:
- 环境心理学环境心理学
- 运输工程 运输工程 运输工程
- 机器学习应用程序 机器学习应用程序
背景情况:
- 高海拔环境带来独特的驾驶挑战,导致司机严重的压力.
- 独特的特点包括山地地形和重型车辆 (卡车和公共汽车) 的更高普及率.
- 现有的评估驾驶压力的方法可能不适合这些恶劣的条件.
研究的目的:
- 通过可见的环境元素,研究预测驾驶员压力水平的可行性.
- 开发和验证一个预测模型,用于在高海拔高速公路设置中的驾驶压力.
- 确定影响驾驶员压力的关键环境因素.
主要方法:
- 在西藏高速公路上收集的自然驾驶数据.
- 使用心率变化 (HRV) 指标和K-平均集群计算量化驾驶压力.
- DeepLabv3模型用于驾驶场景的语义细分;合体学习 (包括极端梯度提升) 用于压力预测.
主要成果:
- 极端梯度增强实现了高预测性能 (F1得分:0.855,G-平均值:0.890).
- 对于卡车/公共汽车变化率 (积极的压力影响) 具有很高的特征重要性.
- 山地,道路和天空特征对压力产生了负面影响;山地元素也通过变化率表现出积极影响.
结论:
- 使用视觉环境元素进行驾驶员压力预测对于高海拔高速公路是可行的和有效的.
- 这种方法为压力评估提供了一种实时,不那么侵入性,更安全的方法.
- 这些发现有助于开发驾驶员状态监测系统和识别高风险路段.
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