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2024 Journal Impact Factor - 0.6
2024 CiteScore - 1.9
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
A Bidirectional Imitation-Learning Framework for Real Time Trajectory Planning in Automatic Berthing
1 Osaka Metropolitan University, Sakai, Osaka, Japan, Osaka, Sakai, Japan
ABSTRACT: This paper presents a bidirectional imitation-learning framework for AIS-based real-time trajectory planning in automatic berthing. The framework connects a ship’s current state with a prescribed terminal berthing condition by using two imitation-learning policies: forward-time imitation learning from the current ship state and backward-time imitation learning from the desired terminal state. The sampled trajectories are converted into time-indexed probability distributions by time-weighted adaptive kernel density estimation, and the integrated trajectory is obtained from the joint distribution of the two directions. This formulation is intended to preserve the feasibility of the initial approach while improving consistency with the final berthing position, heading, and speed. The method was evaluated using actual AIS trajectories of PCC and car ferries berthing at Shinmoji Port, Japan. The results show that the integrated planner reduced the terminal deviations observed in the forward-time planner alone and avoided the initial-state inconsistency observed in the backward-time planner alone. The proposed approach is positioned as a practical data-driven planning framework for real-time berthing support under similar vessel and port conditions.
KEYWORDS: Safety of Navigation, Decision Support System (DSS), Methods and Algorithms, Artificial Intelligence, Autonomous Ships, Navigation, Manoeuvering and Ship-handling Simulation, Marine Traffic Control and Automatic Identification Systems, Manoeuvering in Port Area and Pilot Navigation
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Citation note:
Higaki T., Hashimoto H.: A Bidirectional Imitation-Learning Framework for Real Time Trajectory Planning in Automatic Berthing. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 20, No. 2, doi:10.12716/1001.20.02.14, pp. 397-404, 2026
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