Multi-Modal Attention Networks for Driving Style-Aware Trajectory Prediction in Autonomous Driving

Trajectory prediction is a critical task in autonomous driving systems. It enables vehicles to anticipate the future movements of surrounding traffic participants, which facilitates safe and human-like decision-making in the planning and control layers. However, most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction. These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms, leading to potentially unrealistic predictions. To address these limitations, we propose the Driving Style Guided Trajectory Prediction framework (DSG-TP), which incorporates a probabilistic representation of driving style into trajectory prediction. Our approach enhances the model’s ability to interact with vehicle behavior characteristics in complex traffic scenarios, significantly improving prediction reliability in critical decision-making situations by incorporating the driving style recognition module. Experimental evaluations on the Argoverse 1 dataset demonstrate that our method outperforms existing approaches in both prediction accuracy and computational efficiency. Through extensive ablation studies, we further validate the contribution of each module to overall performance. Notably, in decision-sensitive scenarios, DSG-TP more accurately captures vehicle behavior patterns and generates trajectory predictions that align with different driving styles, providing crucial support for safe decision-making in autonomous driving systems.