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Predicting vehicle trajectories is crucial for ensuring automated vehicleoperation efficiency and safety, particularly on congested multi-lane highways.In such dynamic environments, a vehicle's motion is determined by itshistorical behaviors as well as interactions with surrounding vehicles. Theseintricate interactions arise from unpredictable motion patterns, leading to awide range of driving behaviors that warrant in-depth investigation. This studypresents the Graph-based Interaction-aware Multi-modal Trajectory Prediction(GIMTP) framework, designed to probabilistically predict future vehicletrajectories by effectively capturing these interactions. Within thisframework, vehicles' motions are conceptualized as nodes in a time-varyinggraph, and the traffic interactions are represented by a dynamic adjacencymatrix. To holistically capture both spatial and temporal dependencies embeddedin this dynamic adjacency matrix, the methodology incorporates the DiffusionGraph Convolutional Network (DGCN), thereby providing a graph embedding of bothhistorical states and future states. Furthermore, we employ a drivingintention-specific feature fusion, enabling the adaptive integration ofhistorical and future embeddings for enhanced intention recognition andtrajectory prediction. This model gives two-dimensional predictions for eachmode of longitudinal and lateral driving behaviors and offers probabilisticfuture paths with corresponding probabilities, addressing the challenges ofcomplex vehicle interactions and multi-modality of driving behaviors.Validation using real-world trajectory datasets demonstrates the efficiency andpotential.

Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks