Dual-Encoder Model with Interactive Time Horizon for Vehicle Trajectory Prediction in Dense Traffic

Abstract

Highway ramp merging zones represent a typical dense traffic scenario where complex vehicle interactions make accurate trajectory prediction particularly challenging. This paper proposes a novel dual-encoder model with an interactive time horizon mechanism to address these challenges. The model employs independent encoding channels to separately capture individual motion features and multi-vehicle interaction features, while the introduced selector enables dynamic selection of potentially interacting vehicles. Evaluated on the Chinese highway dataset Expressway-SQM2, our approach demonstrates significant improvements: the dual-encoder architecture alone reduces 5-second prediction RMSE by 29.4% compared with the baseline model, while the complete model with this mechanism achieves a 34.5% reduction. Experimental results confirm the model's effectiveness in handling complex merging scenarios and its strong generalization capability across varying traffic conditions.