IMTP: Intention-Matching Trajectory Prediction for Autonomous Vehicles

Trajectory prediction for surrounding vehicles is critical for ensuring the safety of autonomous driving. In this paper, we introduce a novel prediction framework named Intention-Matching Trajectory Prediction (IMTP). Different from existing results that predict trajectories based on only environmental information and historical trajectories, the proposed method initially identifies the possible intentions of surrounding vehicles based on the environment and generates intention-informed trajectories based on the physical vehicle model. Historical trajectories are then used to identify the intention and trajectory with the highest probability. The proposed framework effectively integrates the physical vehicle model, road-related environmental factors, and interactions among surrounding vehicles. A comparative study conducted on a public dataset demonstrates that our framework enhances both prediction accuracy and robustness.