A novel cluster-based multinomial logit modeling for crash severity analysis in collisions involving automated ev-only manufacturer (AEVOM) vehicles

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The increasing presence of Automated Electric Vehicles-Only Manufacturer (AEVOM) vehicles underscores the need for better understanding of crash severity under partial automation. Utilizing police-reported crash data from Texas between 2017 and 2024, this study applies a two-stage analytical framework to capture heterogeneity in crash outcomes. Variable selection and clustering validity are supported by Extreme Gradient Boosting (XGBoost) feature importance metrics and Cramér's V statistic. Cluster Correspondence Analysis (CCA) is used to classify crashes into four distinct typologies: high-speed highway crashes, intersection-related crashes, low-speed highway crashes with fixed objects, and crashes with parked-vehicles on non-trafficway locations.Within each cluster, Random Parameter Logit (RPL) and RPL with Heterogeneity in Means (RPLHM) models are estimated to account for unobserved heterogeneity in crash severity determinants. The analysis reveals that key variables such as lighting conditions, road classification, driver age, seatbelt use, and vehicle type influence severity outcomes differently across clusters. Notably, severe injuries are observed even in low-speed or seemingly controlled environments, highlighting functional limitations in current AEVOM vehicles' automation systems. This framework improves model fit and interpretability relative to aggregate models and provides insights to advanced driver-assistance systems, infrastructure design, and policy strategies aimed at enhancing the safety of semi-AEVs.

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