Accurate prediction of traffic accident severity remains challenging due to feature coupling and class imbalance, which hinder reliable applications in autonomous driving safety systems. This study proposes a Dynamic and Static Cross Entropy Integrated Neural Network (DSCE-INN) to address these issues. Using 857 real-world accident cases from the 2017-2021 China National Automobile Accident In-Depth Investigation System (NAIS), a Weighted Injury Coefficient is developed to enable continuous injury mapping, and K-means clustering reclassifies severity into three levels: property damage only, non-disabling injury, and disabling or fatal injury. Information gain identifies 11 critical features. DSCE-INN employs feature decoupling, transforming the multi-class task into binary sub-models, and introduces a dynamic-static weighted cross-entropy loss to jointly mitigate coupling and imbalance. A soft-hard voting mechanism, combined with L1 regularisation and focal loss, further enhances prediction robustness. Experimental results show accuracies of 0.782, 0.729, and 0.801, significantly outperforming a baseline ANN. Findings demonstrate DSCE-INN's effectiveness and practical value for autonomous driving safety.
Tan et al. (Thu,) studied this question.