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Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data

Authors Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

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Author Details

Chaithanya Basrur
  • Singapore Management University, Singapore
Arambam James Singh
  • National University of Singapore, Singapore
Arunesh Sinha
  • Singapore Management University, Singapore
Akshat Kumar
  • Singapore Management University, Singapore
T. K. Satish Kumar
  • University of Southern California, Los Angeles, CA, USA

Funding

This research/project is supported by the National Research Foundation Singapore and DSO National Laboratories under the AI Singapore Programme (AISG Award No: AISG2-RP-2020-016).

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Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, and T. K. Satish Kumar. Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 5:1-5:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.CP.2022.5

Abstract

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and runs in near real-time. Second, although automation has improved in maritime operations, current vessel traffic systems (in our case study of a busy Asian port) predict only a single future trajectory of a vessel based on linear extrapolation. Therefore, using historical data we learn a generative model that predicts multiple possible future trajectories of each vessel in a given traffic hotspot, reflecting past vessel movement patterns, and integrate it with our trajectory optimizer. Third, we validate our trajectory optimization and generative model extensively using a real world maritime traffic dataset containing 6 million Automated Identification System (AIS) data records detailing vessel movements over 1.5 years from one of the world’s busiest ports, demonstrating effective risk reduction.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Multi-agent planning
Keywords
  • Multi-Agent Path Coordination
  • Maritime Traffic Control

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Supplementary Materials

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