Interaction-aware Lane-Changing Early Warning System in Congested Traffic

Abstract: Lane changes (LCs) in congested traffic are complex, multi-vehicle interactive events that pose significant safety concerns. Providing early warnings can enable more proactive driver assistance system and support more informed decision-making for drivers under LCs. This paper presents an interaction-aware Lane-Changing Early Warning (LCEW) system designed to issue reliable early warning signals based on future trajectory predictions. We first investigate the stochastic nature of LCs, characterized by (i) variable-size multi-vehicle interactions and (ii) the direct and indirect risks resulting from these interactions. To model these stochastic interactions, a Social Spatio-Temporal Graph Convolutional Neural Network framework informed by mutual information (STGCNN-MI) is introduced to predict multi-vehicle trajectories. By leveraging a MI-based adjacency matrix, the framework enhances trajectory prediction accuracy while providing interpretable representations of vehicle interactions. Then, potential collisions between the LC vehicle and adjacent vehicles (direct risks) or among the non-adjacent vehicles (indirect risks) are identified using oriented bounding box detection applied to the predicted trajectories. Finally, a warning signal is generated to inform the LC driver of location of potential collisions within the predicted time window. Traffic simulation experiments conducted in SUMO demonstrate that the proposed interaction-aware LCEW improves both vehicle-level safety and overall traffic efficiency, while also promoting more natural behavioral adaptation.