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.

该论文提出了一种使用概率时间序列预测将代理安全度量与崩溃概率相关联的方法，并使用 Transformer masked autoregressive flow（Transformer-MAF）估计速度，加速度和时间到碰撞的条件动作概率、崩溃概率和条件崩溃概率。通过反事实实验，条件崩溃概率显示了规避碰撞的有效性。