arXiv:2608. 19580v1 Announce Type: new Abstract: Vessel trajectory prediction is critical for maritime safety and accident prevention.
arXiv:2608. 10256v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and navigational realism.
arXiv:2606. 15240v2 Announce Type: replace Abstract: Accurate vessel trajectory forecasting is essential for maritime situational awareness, navigation safety, traffic management, and autonomous navigation.
arXiv:2606. 06311v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is essential for safe and efficient maritime operations, enabling collision avoidance and supporting route optimization.
arXiv:2603. 28067v2 Announce Type: replace Abstract: Digital testing has emerged as a key paradigm for the development and verification of autonomous maritime navigation systems, yet the availability of realistic and diverse safety-critical encounter scenarios remains limited.
arXiv:2607. 18887v1 Announce Type: new Abstract: Vessel trajectory prediction in complex maritime environments is essential for traffic management, collision warning, route planning, and autonomous navigation.
arXiv:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.
arXiv:2606. 15240v1 Announce Type: new Abstract: Vessel trajectory prediction is important for intelligent shipping, maritime surveillance, and navigation safety.
arXiv:2601. 18537v4 Announce Type: replace-cross Abstract: Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors.
arXiv:2606. 17978v1 Announce Type: new Abstract: Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection.
arXiv:2108. 04694v2 Announce Type: cross Abstract: We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras.
This paper presents Transformer and Performer models for forecasting surface vessel trajectories in confined waters using AIS position data. It introduces positional heat maps generated via Kernel Density Estimation and encoded with a CNN to incorporate local vessel movement patterns. The models, trained with quantile loss, achieve superior performance over prior work and provide prediction intervals essential for collision avoidance.
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.