Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer-MAF). The autoregressive structure mimicked the causal relationship between condition, action and crash outcome and the probability density functions are used to calculate the conditional action probability, crash probability and conditional crash probability. The predicted sequence is accurate and the estimated probability is reasonable under both traffic conflict context and normal interaction context and the conditional crash probability shows the effectiveness of evasive action to avoid crashes in a counterfactual experiment.
翻译:代用安全措施可以提供快速和主动的安全分析,并通过研究近乎失手的情况来深入了解坠毁前过程和坠毁失灵机制。然而,通过将其与坠毁联系起来来验证代用安全措施仍然是一个尚未解决的问题。本文件提出了一个方法,将代用安全措施与使用概率时间序列预测的坠毁概率联系起来。该方法使用了速度、加速和时间到时间的顺序来估计这些变数的概率密度功能,这些变压器掩盖了自动反反向回流( Transfred-MAF)。自动反向结构模拟了条件、动作和坠毁结果之间的因果关系以及概率密度功能,用来计算有条件的行动概率、坠毁概率和有条件的碰撞概率。预测的顺序准确,估计概率在交通冲突背景下和正常互动背景下以及有条件的碰撞概率都是合理的。该方法用速度、加速和时间到的顺序来估计这些变压动作避免在反事实实验中碰撞的效能。