A deep neural network and AIS-integrated method for ship trajectory prediction and yaw warning in cross-river power transmission line protection

Abstract

Ship trajectory prediction plays a crucial role in ensuring the safety of inland waterway transportation and enabling intelligent scheduling. To address the limitations of traditional models in capturing long-term dependencies and extracting salient features, this study proposes a novel prediction model—TDV-TTCN-BiGRU—integrating a Temporal-Dependent Variable (TDV) attention mechanism with an improved TCN-BiGRU architecture. The model employs a hierarchical Temporal Convolutional Network (TTCN) to extract multi-scale temporal features in parallel, incorporates the TDV attention mechanism to adaptively adjust the weights of speed and heading features, and uses BiGRU to model bidirectional dependencies, thereby enhancing prediction accuracy and stability. Experiments based on real AIS data include both comparative and ablation studies. Results show that the proposed TCN-BiGRU outperforms CNN, BiGRU, CNN-BiLSTM, and CNN-BiGRU models, achieving the lowest prediction errors. Compared to CNN-BiGRU, the TDV-TTCN-BiGRU model reduces MSE of predicted longitude and latitude by 14.47% and 18.83%, respectively; MAE by 22.41% and 21.25%; and ADE by 21.46%, with trajectory plots showing closer alignment with actual vessel tracks. Furthermore, to address the risk of vessels deviating from navigable channels, a multi-level yaw warning mechanism is developed and validated in typical cross-river scenarios. The system achieves a warning accuracy of over 96%, significantly improving the responsiveness to unexpected yaw behavior. The proposed method provides technical support for intelligent ship navigation, maritime safety management, and the protection of overhead transmission lines.