Highlights This paper introduces a procedure to specify crash scenarios for safety evaluation of automated vehicles. Intersection two-vehicle crash scenarios are specified using the procedure. Crash sequence analysis characterizes the crashes as 55 types based on their sequence patterns. Bayesian network models relationships between crash sequence types, crash outcomes, and describing operational design domain variables. Scenarios are specified by querying the Bayesian network for conditional probability distributions.

    Abstract This paper introduces a test scenario specification procedure using crash sequence analysis and Bayesian network modeling. Intersection two-vehicle crash data was obtained from the 2016–2018 National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS) database. Vehicles involved in the crashes are specifically renumbered based on their initial positions and trajectories. Crash sequences are encoded to include detailed pre-crash events and concise collision events. Based on sequence patterns, the crashes are characterized as 55 types. A Bayesian network model is developed to depict the interrelationships among crash sequence types, crash outcomes, human factors, and environmental conditions. Scenarios are specified by querying the Bayesian network’s conditional probability table. Distributions of operational design domain (ODD) attributes (e.g., driver behavior, weather, lighting condition, intersection geometry, traffic control device) are specified based on conditions of sequence types. Also, distribution of sequence types is specified on specific crash outcomes or combinations of ODD attributes.


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    Title :

    Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks


    Contributors:


    Publication date :

    2022-08-16




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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