RAGAN: A Generative Adversarial Network for risk-aware trajectory prediction in multi-ship encounter situations

Ship trajectory prediction plays a vital role in ensuring safety in dense waterway traffic. However, conventional prediction methods often disregard the intricate spatial-temporal interactions and the inherent collision avoidance maneuvers that occur during multiple ship encounters, leading to inadequate accuracy in interacted trajectory predictions. To address this challenge, we propose a risk-aware trajectory prediction framework based on Generative Adversarial Network (RAGAN) architectures. RAGAN leverage GANs to learn latent collision avoidance interaction patterns in multi-ship encounters. Through adversarial training, RAGAN continuously enhances its capability to generate precise and safely acceptable ship trajectories that faithfully represent the actual collision avoidance strategies adopted by ship officers. Furthermore, by incorporating an interacting gate function, RAGAN effectively captures the evolving risks and influences within encounter situations, enabling dynamic representation of ship interactions and their profound impact on the development of encounters. Evaluation using real-world ship trajectory data demonstrates the superior performance of RAGAN compared to the baseline method across various evaluation metrics. Through case studies, RAGAN exhibits the ability to learn navigation rules beyond existing regulations, such as the COLREGs, and provides valuable insights for enhancing safety in maritime operations.

Automated summary

Ship trajectory prediction is crucial for ensuring safety in dense waterway traffic. This paper proposes a risk-aware trajectory prediction framework, RAGAN, based on Generative Adversarial Network, to accurately predict ship trajectories and collision avoidance strategies in multi-ship encounters. Through adversarial training and an interacting gate function, RAGAN effectively captures risks and influences in encounter situations, demonstrating the ability to learn beyond existing regulations.