论文标题
通过因果概率时间序列预测将替代安全措施连接到崩溃概率
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction
论文作者
论文摘要
替代安全措施可以提供快速和积极的安全性分析，并通过研究近未错过来就撞车过程和崩溃失败机制提供见解。但是，通过将替代安全措施连接到崩溃仍然是一个悬而未决的问题。本文提出了一种连接替代安全措施的方法，以使用概率时间序列预测来崩溃概率。该方法使用了速度，加速度和汇合时间的序列来估计这些变量使用变压器掩盖自回旋流动流（变压器-MAF）的概率密度函数。自回归结构模仿了条件，动作和崩溃结果与概率密度功能之间的因果关系，以计算有条件的动作概率，崩溃概率和有条件的崩溃概率。预测的序列是准确的，在交通冲突环境和正常交互环境下，估计的概率是合理的，有条件的崩溃概率显示了反复作用的有效性，以避免在反事实实验中崩溃。
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.
