With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages.

Knowledge distillation has been one of the most effective methods for enhancing the performance of lightweight detectors, crucial for remote sensing edge intelligence models. However, many mainstream distillation methods that are centered around the paradigm of distilling positive sample show weak exploitation of the student’s potential. This arises due to these methods overlooking the core teacher-student difference in remote sensing scenarios with vast and object-similar background. In this article, from the point of distillation sample and knowledge hierarchy, we design a negative-core sample knowledge distillation (NSD) method for improving the performance of the lightweight object detection model. Specifically, a negative-core sample (NCS) is innovatively employed to transfer effective background discrimination knowledge for bridging the core difference. Knowledge distillation for NCS across four levels—pixel, logit, box, and angle are customized to fully leverage the teacher’s insights. Category direction estimation(CE) is incorporated into the angle knowledge distillation to more effectively convey NCS oriented knowledge in a discrete manner. Extensive experiments conducted on multiple remote sensing datasets achieve state-of-the-art performance, demonstrating the effectiveness of the proposed NSD. Codes are available at https://github.com/Changan00/NSD.

Trajectory prediction has become increasingly critical in various applications such as autonomous driving and robotic navigation. However, due to the significant variations in trajectory patterns across different scenarios, models trained in known environments often falter in unseen ones. To learn a generalized model that can directly handle unseen domains without requiring any model updating, we propose a novel tailored meta-learning-based trajectory prediction model called DTM. Our approach integrates a dual trajectory transformer (Dual_TT) equipped with an agent-consistency loss, facilitating a comprehensive exploration of both individual intentions and group dynamics across diverse scenarios. Building on this, we propose a tailored meta-learning framework (TMG) to simulate the generalization process between source and target domains during the training phase. In the task construction phase, we employ multi-dimensional labels to precisely define and distinguish between different domains. During the dual-phase parameter update, we partially fix crucial attention mechanism parameters and apply an attention alignment loss to harmonize domain-invariant and specific features. We also incorporate a Serial and Parallel Training (SPT) strategy to significantly enhance task processing and the model’s adaptability to domain shifts. Extensive testing across various domains demonstrates that our DTM model not only outperforms existing top-performing baselines on real-world datasets but also validates the effectiveness of our design through ablation studies.