分析自动驾驶汽车防御制动引起的二次风险:基于司机随机分布的研究
Tingyu Liu1, Zhenyu Zhao1, Miaomiao Yang2
1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China; College of Transportation, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.
Accident; analysis and prevention
|July 27, 2025
概括
自动驾驶汽车的防御制动可以增加后部碰撞的风险. 一个新的随机追踪模型 (SFM) 显示,连续制动在混合交通中显著提高了碰撞概率.
科学领域:
- 交通安全 交通安全 交通安全
- 自主驾驶系统 自主驾驶系统
- 行为建模行为建模
背景情况:
- 自动驾驶汽车 (AV) 的防御制动提高了安全性,但可能导致后方碰撞,这是AV中常见的一种涉及人类驾驶员的事故类型.
- 评估后方碰撞风险是具有挑战性的,因为人类驾驶员的后续行为存在不确定性.
研究的目的:
- 开发一种特征驾驶员行为模型,以模拟和评估自动驾驶汽车防御制动对后方碰撞可能性的影响.
- 为了验证驾驶员风险容忍水平 (RTL) 遵循正常分布的假设.
主要方法:
- 基于风险恒温理论和中央极限定理提出了一个假设,即驱动器RTL遵循正常分布.
- 使用Waymo数据集验证了假设,开发了一个随机追随模型 (SFM).
- 利用蒙特卡洛模拟来评估各种场景中的碰撞概率.
主要成果:
- SFM准确地表示混合交通中的司机随机分布和多样性,优于智能驾驶模型 (IDM).
- 无人驾驶车辆短暂的防御制动对接下来的车辆碰撞概率的影响很小.
- 自动驾驶车辆持续的防御制动大大增加了被后方撞击的可能性.
结论:
- 该SFM有效地捕捉到驾驶员的多样性和随机性质,在混合交通中遵循行为.
- 无人驾驶汽车的持续防御制动带来了严重的后方碰撞风险.
- SFM为未来研究混合交通流安全提供了宝贵的参考.
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