mlsanityData to 14 Oct 2026

Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction

arXiv 2210.01363 ↗First posted 4 Oct 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 66 references that are in the map. First posted on arXiv 4 Oct 2022.

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13 of 66 references.

References · 13

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  12. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 144
1 more
  1. 2020Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Methodcited by 75

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