查看文章

Automated vehicles (AVs) represent an opportunity to reduce crash frequency by eliminating driver error, as safety studies reveal human error contributes to the majority of crashes. To provide insights into the contributing factors of AV crashes, this study created a unique database from the California Department of Motor Vehicles 124 manufacturer-reported Traffic Collision Reports and was linked with detailed data on roadway and built-environment attributes. A novel text analysis was first conducted to extract useful information from crash report narratives. Of the crashes that could be geocoded (N = 113), results indicate the most frequent AV crash type was rear-end collisions (61.1%; N = 69) and 13.3% (N = 15) were injury crashes. These noteworthy outcomes and a small sample size motivated us to rigorously analyze rear-end and injury crashes in a Full Bayesian empirical setup. Owing to the potential issue of …