Analysis of Cascading Conflict Risks of Autonomous Vehicles in Heterogeneous Traffic Flows

As autonomous vehicles proliferate in mixed traffic streams, heterogeneous flows comprising vehicles with diverse driving strategies introduce significant complexity to cascading conflict propagation, while conventional conflict risk assessment methods based on homogeneous assumptions fail to capture the intricate risk transmission mechanisms embedded in high-dimensional trajectory data. To address the challenge, this study establishes a systematic data analytics framework. Firstly, a conflict risk quantification model is proposed by integrating safety field theory considering heterogeneity traffic flow, achieving precise quantification of microscopic interaction risks through vehicle risk coefficients that characterize differential risk sensitivity across distinct driving strategies. Secondly, a cascading conflict identification algorithm is designed to extract cascading propagation chains from trajectory data. Thirdly, a method to analyze cascading conflict risk propagation is developed using CatBoost (v1.2.8), coupled with SHapley Additive ExPlanations interpretability analysis to systematically reveal the propagation mechanisms underlying cascading conflicts. Empirical findings indicate that primary conflict intensity and longitudinal relative speed are the dominant predictive features for secondary conflicts; moreover, local traffic heterogeneity entropy exerts a significant moderating effect—quantitative analysis reveals that higher heterogeneity increases the likelihood of secondary conflicts under identical primary risk conditions. Comprehensive validation using SUMO microscopic simulation demonstrates that the proposed data analytics pipeline effectively identifies and accurately predicts and analyzes secondary conflicts across diverse traffic scenarios. This framework provides interpretable foundations for intelligent conflict-risk identification systems, propagation-mechanism analysis, and proactive safety interventions in heterogeneous traffic environments, offering significant implications for real-time traffic monitoring and intelligent transportation system design.