What can we learn from autonomous vehicle collision data on crash severity? A cost-sensitive CART approach

Abstract

Autonomous vehicles (AVs) are emerging in the automobile industry with potential benefits to reduce traffic congestion, improve mobility and accessibility, as well as safety. According to the AV collision data managed by the California Department of Motor Vehicles (DMV), however, the safety issue of AVs has continuously been a concern. This paper aims to learn the contributing factors to AV crash severity from the latest 3-year AV collision data. To achieve the objective, we develop an AV crash severity classification tree with the possible contributing factors by the cost-sensitive classification and regression tree (CART) model, which can deal with the class imbalance issue raised from the AV collision dataset. Our results show that the main factors affecting AV crash severity level include manufacturer, facility type, movement preceding collision, collision type, light condition and year. These findings could provide useful insights for traffic engineers or AV manufacturers to raise effective counter measures or policies to mitigate AV crash severity.