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  • Title: Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology.
    Author: Liu Q, Wang X, Wu X, Glaser Y, He L.
    Journal: Accid Anal Prev; 2021 Sep; 159():106281. PubMed ID: 34273622.
    Abstract:
    Data-based research approaches to generate crash scenarios have mainly relied on conventional vehicle crashes and naturalistic driving data, and have not considered differences between the autonomous vehicle (AV) and conventional vehicle crashes. As the AV's presence on roadways continues to grow, its crash scenarios take on new importance for traffic safety. This study therefore obtained crash patterns using the United States Department of Transportation pre-crash scenario typology, and used statistical analysis to determine the differences between AV and conventional vehicle pre-crash scenarios. Analysis of 122 AV crashes and 2084 conventional vehicle crashes revealed 15 types of scenario for AVs and 26 for conventional vehicles. The two groups showed differences in type of scenario, and differed in the proportion of crashes when the scenario was the same. The most frequent AV pre-crash scenarios were rear-end collisions (52.46%) and lane change collisions (18.85%), with the proportion of AVs rear-ended by conventional vehicles occurring with a frequency 1.6 times that of conventional vehicles. An in-depth crash investigation was conducted of the characteristics and causes of four AV pre-crash scenarios, summarized from the perspectives of perception and path planning. The perception-reaction time (PRT) difference between AVs and human drivers, AV's inaccurate identification of the intention of other vehicles to change lanes, and AV's insufficient path planning combining time and space dimensions were found to be important causes for the AV crashes. By increasing understanding of the complex characteristics of AV pre-crash scenarios, this analysis will encourage cooperation with vehicle manufacturers and AV technology companies for further study of crash causation toward the goals of improved test scenario construction and optimization of the AV's automated driving system (ADS).
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