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A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic
Norris Novat1, Emmanuel Kidando2, Boniphace Kutela3
1Graduate Research Assistant, Cleveland State University, 2121 Euclid Avenue, Cleveland, OH 44115, United States.
Journal of Safety Research
|March 3, 2023
Summary
Automated vehicles (AVs) show a higher likelihood of rear-end collisions but are safer in other crash types. Further research is needed to improve AV safety, especially at intersections and lower speeds.
Area of Science:
- Traffic Safety
- Automated Vehicle Technology
- Collision Analysis
Background:
- Automated vehicle (AV) technology promises enhanced traffic efficiency and safety by reducing human error.
- Limited crash data and fewer AVs on roads necessitate further investigation into AV-specific safety concerns.
Purpose of the Study:
- To conduct a comparative analysis of collision factors between AVs and conventional vehicles.
- To identify specific collision types and contributing factors associated with AVs.
Main Methods:
- Utilized a Bayesian Network (BN) model fitted with Markov Chain Monte Carlo (MCMC).
- Analyzed four years (2017-2020) of crash data from California, including 127 AV and 865 conventional vehicle incidents.
- Associated AV and conventional vehicle crashes using a 50-foot buffer.
Main Results:
- AVs are 43% more likely to be involved in rear-end collisions compared to conventional vehicles.
- AVs demonstrate a 16% lower likelihood of sideswipe/broadside and a 27% lower likelihood of other collision types.
- Increased risk of rear-end collisions for AVs is linked to signalized intersections and speed limits below 45 mph.
Conclusions:
- AVs contribute to improved road safety by reducing human error in most collision types.
- Current AV technology requires further advancements to address safety challenges, particularly concerning rear-end collisions.
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