The Extraction of Automated Vehicles Traffic Accident Factors and Scenarios Using Real-World Data

Abstract

As automated vehicles (AVs) approach commercialization, the fact that the SAFETY problem becomes more concentrated is not controversial. Depending on this issue, the scenarios research that can ensure safety and are related to vehicle safety assessments are essential. In this paper, based on ‘report of traffic collision involving an AVs’ provided by California DMV (Department of Motor Vehicles), we extract the major factors for identifying AVs traffic accidents to derive basic AVs traffic accident scenarios by employing the random forest, one of the machine learning. As a result, we have found the importance of the pre-collision movement of neighboring vehicles to AVs and inferred that they are related to collision time (TTC). Based on these factors, we derived scenarios and confirm that AVs rear-end collisions of neighboring vehicles usually occur when AVs are ahead in passing, changing lanes, and merge situations. While most accident determinants and scenarios are expected to be similar to those between human driving vehicles (HVs), AVs are expected to reduce accident rates because ‘AVs do not cause accidents.’ © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.