Vessel trajectory prediction is pivotal for maritime traffic safety and autonomous collision avoidance. However, existing studies predominantly rely on massive public AIS (Automatic Identification System) datasets, often overlooking the challenges of data sparsity and long-tailed distributions inherent in practical engineering scenarios, where high-dynamic maneuvering samples are scarce. Furthermore, as a low-frequency kinematic observation system, AIS suffers from inherent kinematic lag relative to the vessel’s true dynamic state, particularly failing to timely reflect turning intentions during the maneuver initiation phase. To address these challenges, this paper proposes a Physics-Aware Multimodal Fusion Framework. By incorporating high-frequency acceleration and angular velocity from an Inertial Measurement Unit (IMU), the framework applies physical compensation to AIS kinematic observations, thereby enhancing the model’s perception of maneuvering intent. Validation based on real-vessel experimental data demonstrates that the proposed method effectively mitigates the prediction lag observed in pure-AIS models at the 60 s horizon, significantly improving accuracy in turning scenarios. Moreover, in 180 s long-term predictions, the multimodal fusion mechanism effectively suppresses integration drift, ensuring superior trajectory shape consistency and engineering stability. The study indicates that incorporating IMU inertial information is critical for enhancing the robustness of vessel trajectory prediction under practical engineering conditions characterized by sparse samples and complex maneuvers.
Zhang et al. (Sat,) studied this question.