  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.
