反事实接近加速度风险：一种适用于跟驰盲区的预测性替代安全措施

中文摘要 AI 辅助

替代安全措施可利用无碰撞的轨迹数据开展道路安全评估，但主流的接近度指标——碰撞时间（TTC）及其变体——假设运动不变，且当前车未快于前车时无定义，因此大量跟驰场景根本无法获得风险读数。本文提出反事实接近加速度风险（CCAR），这是一种预测性替代指标，用于评分当前车在当前接近加速度条件下，若前车制动时发生追尾冲突的暴露程度。在SQM-W-1轨迹数据集的745540个高速公路跟驰帧上对CCAR进行评估，传统指标因当前车未快于前车而留下51%未评分的帧，而在该盲区内，CCAR在98.8%的帧中返回了分级的非平凡风险。CCAR与现有指标不冗余（与改进TTC的斯皮尔曼相关系数为0.54，前十分位风险集重叠度为0.19），且在固定间距和速度下，其风险随接近加速度单调上升。采用反应式智能驾驶员模型（IDM）跟驰车和脚本式前车制动的受控仿真证实，在固定间距和制动条件下，实际碰撞率随接近加速度上升，支持所提出的前驱机制。文中还讨论了参数敏感性、局限性及对前向碰撞预警系统的意义。

英文摘要

Surrogate safety measures allow road-safety assessment from trajectory data in which crashes are absent, yet the dominant proximity measures - time-to-collision (TTC) and its variants - assume invariant motion and are undefined whenever the following vehicle is not yet faster than its leader, so a large fraction of car-following carries no risk reading at all. This paper introduces Counterfactual Closing-Acceleration Risk (CCAR), an anticipatory surrogate measure that scores how exposed a follower is to a rear-end conflict if its leader were to brake, conditioned on the follower's current gap-closing acceleration. CCAR is evaluated on 745,540 expressway car-following frames from the SQM-W-1 trajectory dataset. Conventional measures leave 51% of frames unscored because the follower is not yet faster; across this blind region CCAR returns a graded, non-trivial risk in 98.8% of frames. CCAR is not redundant with existing measures (Spearman correlation 0.54 with modified TTC; top-decile risk-set overlap 0.19), and at fixed gap and speed its risk rises monotonically with closing acceleration. A controlled simulation with a reactive Intelligent Driver Model follower and a scripted leader brake confirms that, holding gap and brake fixed, the actual collision rate rises with closing acceleration, supporting the proposed precursor mechanism. Parameter sensitivity, limitations, and implications for forward-collision-warning systems are discussed.

发表机构

• Chang’an University（长安大学）

机构由 AI 辅助整理，请以论文原文为准。