4.6 Article

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

Journal

JOURNAL OF SAFETY RESEARCH
Volume 84, Issue -, Pages 251-260

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsr.2022.11.001

Keywords

Automated vehicles; Traffic crashes severity; Bayesian networks

Ask authors/readers for more resources

Automated vehicle technology is promising for improving traffic efficiency and reducing emissions. This study compares automated vehicles and conventional vehicles in different types of collisions, finding that automated vehicles are more likely to be involved in rear-end crashes but less likely to be involved in other types of collisions. Safety aspects of automated vehicles need improvement.
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 associ-ated 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, hit-ting an object, etc.), respectively, when compared to conventional vehicles. The variables associated with the increased likelihood of rear-end collisions for AVs include signalized intersections and lanes with less than 45 mph speed limit.Conclusions: Although AVs are found to improve safety on the road in most types of collisions by limiting human error leading to vehicle crashes, the current state of the technology shows that safety aspects still need improvement.(c) 2022 National Safety Council and Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available