Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v38i21.30439Keywords:
Sequential Modeling, Transformer Architecture, Offline RL Dataset, Marine Navigation, Gym Environment, Auto-regressive ModelAbstract
The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption. This paper undertakes this challenge through a machine learning approach, leveraging a real-world dataset spanning two years of a passenger vessel in west coast Canada. Our focus centers on the creation of a time series forecasting model given the dynamic and static states, actions, and disturbances. This model is designed to predict dynamic states based on the actions provided, subsequently serving as an evaluative tool to assess the proficiency of the vessel's operation under the captain's guidance. Additionally, it lays the foundation for future optimization algorithms, providing valuable feedback on decision-making processes. To facilitate future studies, our code is available at https://github.com/pagand/model_optimze_vessel/tree/AAAI.Downloads
Published
2024-03-24
How to Cite
Fan, Y., Agand, P., Chen, M., Park, E. J., Kennedy, A., & Bae, C. (2024). Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23484-23485. https://doi.org/10.1609/aaai.v38i21.30439
Issue
Section
AAAI Student Abstract and Poster Program