Enhancing Maritime Navigational Safety
Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data
Bibliographic Data
| ID | 22034844 |
|---|---|
| Authors | Mingze Li (0000-0001-8769-4970, Harbin Engineering University), Bing Li (0000-0002-0238-834X, Harbin Engineering University), Zhigang Qi (0009-0005-8265-1820, Harbin Engineering University, corresponding author), Jiashuai Li (0009-0002-6520-1471, Harbin Engineering University), Jiawei Wu (0000-0002-3702-292X, Harbin Engineering University) |
| Year | 2024 |
| Volume | 13 |
| Issue | 3 |
| Pages | 85 |
| Publication date | 2024-03-08 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13030085 |
| OpenAlex | W4392596920 |
| Language | EN |
| Citations received | 3 |
| References cited | 30 |
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability
Aeronautics · Automatic Identification System · Marine engineering · Physics · Real-time computing · Trajectory · Computer Science · Engineering · Maritime Navigation and Safety · Ship Hydrodynamics and Maneuverability · Structural Integrity and Reliability Analysis · Artificial Intelligence
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |