综合驾驶风险替代模型和汽车跟踪行为,用于高速公路风险评估
Renfei Wu1, Linheng Li2, Haotian Shi3
1School of Transportation, Southeast University, Nanjing, China; Institute of Transport Studies, Monash University, Australia; Joint Research Institute on Internet of Mobility between Southeast University and University of Wisconsin-Madison, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, Nanjing, China.
一种新的驾驶风险替代 (DRS) 模型准确预测车辆轨迹和速度,提高交通安全. 这种先进的风险评估方法提高了对驾驶员互动和实时汽车追踪行为的理解.
科学领域:
- 交通工程是交通工程.
- 交通运输中的人类因素
- 计算建模 计算建模
背景情况:
- 驾驶员的风险感知对于理解车辆互动和汽车跟踪行为至关重要.
- 实时风险评估对于提高交通安全至关重要,这是技术进步所实现的.
- 现有的风险模型可能无法完全捕捉驾驶相互作用的复杂性.
研究的目的:
- 提出一种新的方法来评估高速公路上的驾驶互动风险.
- 开发和验证一个新的模型,驾驶风险代用 (DRS),整合风险感知和汽车跟踪行为.
- 与传统方法相比,评估DRS模型的准确性和有效性.
主要方法:
- 开发了基于潜在场理论的驾驶风险替代 (DRS) 模型,结合了虚拟能量属性.
- 使用子模型量化风险因素:交互车辆风险,限制风险和速度风险替代品.
- 将DRS模型应用于高速公路场景,进行灵敏度分析,并使用自然驾驶数据校准汽车追随行为.
主要成果:
- 与其他方法相比,DRS模拟的汽车追踪行为显示出优越的轨迹预测和速度估计.
- DRS风险评估显示,在自由流动和拥堵交通状态中估计风险水平的准确性更高.
- 与传统模型 (TTC,DRAC,MTTC,DRPFM) 相对验证证实了DRS模型的增强精度.
结论:
- 拟议的驾驶风险代用模型 (DRS) 为评估驾驶互动风险提供了更准确的方法.
- DRS模型有效地描述了数字模拟中的车辆交互和汽车追踪行为.
- 这项研究为交通安全和智能交通系统的研究人员和从业人员提供了宝贵的工具.
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