Traffic risk spatial-temporal evolution mechanism and dynamic prediction approach from the vehicle-to-vehicle...

In intelligent connected vehicles, lane-changing decisions and trajectory planning often prioritize local safety, neglecting the broader impact of vehicle interactions on traffic flow. However, interaction patterns between vehicles significantly affect both lane-change risk and traffic dynamics. Moreover, existing models often overlook spatial dependencies in highly interactive environments. This study addresses these gaps by proposing a classification of four interaction patterns based on vehicle acceleration and inter-vehicle gap trends. Using the TOD trajectory dataset, we analyze the dynamic evolution of traffic risk across these patterns and introduce a spatiotemporally-aware risk prediction model. A Spatial Durbin Model captures the spatial influence of neighboring vehicles on ego-vehicle risk, while a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) dynamically adjusts transition probabilities to forecast risk levels and vehicle motion states. This integrated approach enables short-term, macroscopic traffic flow risk forecasting. Results show that aligned interaction intentions lead to slower risk dissipation and higher risk levels. Compared to the GMM-HMM and Recurrent Neural Network (RNN) models that do not account for spatial effects, the proposed model achieves a prediction accuracy of 85.5% in a 5-second prediction window, which is 13.5% and 5.0% higher, respectively. Although it is slightly lower than the 86.5% achieved by the Temporal Fusion Transformer (TFT), the computational time is reduced by 1.814 s, demonstrating better real-time performance. The model shows good prediction accuracy across four interaction modes: steady-state interaction, strong competitive interaction, strong cooperative interaction, and weak cooperative interaction. The accuracy rates for predicting the risk of subsequent vehicles within 5 s are 87.8%, 76.4%, 84.7%, and 81.9%, respectively. This study provides valuable insights into the mechanisms through which different vehicle interaction patterns influence traffic flow, and offers essential guidance and optimization directions for lane-change trajectory planning in autonomous driving systems.