A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences

•This research focuses on giving a comparative examination of the variables contributing to various types of crashes between automated vehicles and conventional vehicles.•A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective.•The study used four years (2017–2020) of AV and conventional vehicle crash data on California roads. The AV crash dataset was acquired from the California Department of Motor Vehicle, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database.•Along with other findings, our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes.•AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles.•Despite the fact that AVs increase road safety in most kinds of crashes by reducing human error leading to vehicle crashes, the current state of the technology demonstrates that safety elements still need development. Introduction: Automated vehicle (AV) technology is a promising technology for improving the efficiency of traffic operations and reducing emissions. This technology has the potential to eliminate human error and significantly improve highway safety. However, little is known about AV safety issues due to limited crash data and relatively fewer AVs on the roadways. This study provides a comparative analysis between AVs and conventional vehicles on the factors leading to different types of collisions. Method: A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. Four years (2017–2020) of AV and conventional vehicle crash data on California roads were used. The AV crash dataset was acquired from the California Department of Motor Vehicles, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. A buffer of 50 feet was used to associate each AV crash and conventional vehicle crash; a total of 127 AV crashes and 865 conventional vehicle crashes were used for analysis. Results: Our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. Further, AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.),