Determination of functional scenarios for intersection collisions

• Published in: Accident analysis and prevention 2023-12, Vol.193, p.107326-107326, Article 107326
• Main Authors: Bangert, L. Garrett, Lubash, Theodore, Scanlon, John M., Kusano, Kristofer D., Riexinger, Luke E.
• Format: Article
• Language: English
• Subjects: ADS; CISS; I-ADAS; Intersection crashes; Unsupervised decision trees
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2023.107326

•An unsupervised decision tree characterized intersection crash data structures.•There were five main intersection crash data structures.•A travel lane violation best categorized crashes within the data structures. The National Highway Traffic Safety Administration (NHTSA) estimated that in 2019, intersection crashes accounted for $179 billion of economic damages and $639 billion in societal damages. Intersection advanced driver assist systems (I-ADASs) and automated driving systems (ADS) are designed and have been actively deployed to avoid or mitigate these intersection crash scenarios. Given the indeterminate parameter space for describing collision scenarios, evaluators, and designers are all challenged with condensing the possible intersection crash configurations into digestible, executable conditions for scenario-based simulation testing. The objective of this study is to identify functional intersection crash configurations for I-ADAS and ADS safety evaluation. Real-world intersection crash characteristics are important considerations for scenario testing as these features can directly correlate to or influence causality, controllability, and potential injury severity. To identify functional intersection crash types, similar crash scenarios were grouped together by identified critical features using an unsupervised decision tree model. A key advantage of this approach was that the implemented cluster crash scenarios would be understandable and interpretable by users. Unsupervised decision trees work by generating uniformly distributed synthetic data with features from real data and classifying all the data as real or synthetic. Long, non-diverging branches were manually pruned to reduce overfitting and improve model performance. Feature importance values were computed based on how effective a given variable grouped the crashes together. This analysis selected intersection cases that only involved two vehicles from the Crash Investigation Sampling System (CISS) spanning 2017 to 2020. Crash features such as road geometry, intersection signal, and vehicle configuration were important to consider for scenario generation. CISS contained the traffic device, device functionality, vehicle intended pre-event movement, road alignment, road profile, trafficway flow, number of lanes, and crash type for each crash case. Intersection geometry, intersecting road angle, each vehicles’ legal moves, and the presence of a two-way-left-turn-lane (TWLTL), channelized r