Highlights • We explored Autonomous Vehicle (AV) crashes involving vulnerable road users (VRUs) • We applied text network and four text classifiers. • Unlike pedestrians, bicyclists and scooterists are likely to be at fault and directly involved. • Crashes involving VRUs indirectly are likely to occur when AVs are in autonomous mode. • Intersection-related features are the key predictors of the VRUs-AV related crashes.

Abstract Autonomous or automated vehicles (AVs) have the potential to improve traffic safety by eliminating majority of human errors. As the interest in AV deployment increases, there is an increasing need to assess and understand the expected implications of AVs on traffic safety. Until recently, most of the literature has been based on either survey questionnaires, simulation analysis, virtual reality, or simulation to assess the safety benefits of AVs. Although few studies have used AV crash data, vulnerable road users (VRUs) have not been a topic of interest. Therefore, this study uses crash narratives from four-year (2017–2020) of AV crash data collected from California to explore the direct and indirect involvement of VRUs. The study applied text network and compared the text classification performance of four classifiers - Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), and Neural Network (NN) and associated performance metrics to attain the objective. It was found that out of 252 crashes, VRUs were, directly and indirectly, involved in 23 and 12 crashes, respectively. Among VRUs, bicyclists and scooterists are more likely to be involved in the AV crashes directly, and bicyclists are likely to be at fault, while pedestrians appear more in the indirectly involvements. Further, crashes that involve VRUs indirectly are likely to occur when the AVs are in autonomous mode and are slightly involved minor damages on the rear bumper than the ones that directly involve VRUs. Additionally, feature importance from the best performing classifiers (RF and NN) revealed that crosswalks, intersections, traffic signals, movements of AVs (turning, slowing down, stopping) are the key predictors of the VRUs-AV related crashes. These findings can be helpful to AV operators and city planners.