Work

Intention-aware prediction for collaborative collision avoidance in manned-unmanned mixed maritime traffic

Year: 2026

Type: article

Abstract: Communication between vessels is critical for successful collision avoidance. In conventional marine navigation, where all vessels have human crew on board, such a task is trivial. However, with the rise of interest in autonomous vessels, we could expect marine traffic scenarios where both manned and unmanned vessels co-exist for a long period of time until the entire maritime industry fully transitions to autonomous vessels. Implementing a collaborative collision avoidance algorithm in an unmanned vessel in such an environment could be challenging due to the lack of modern computational equipment on board a traditional manned vessel. In this paper, we present an approach that allows collaborative collision avoidance between manned and unmanned vessels. We extend an existing collaboration framework designed for unmanned vessels utilizing a Dynamic Bayesian Network (DBN) to model the response behavior of the manned vessel, enabling manned and unmanned vessel collaboration. A simulation study is used to verify the applicability of the proposed approach and to evaluate its performance.

Source: Ocean Engineering

DOI: 10.1016/j.oceaneng.2026.125131

Authors: Dhanika Mahipala, Hoang Anh Tran, Rana Saha, Tor Arne Johansen

Institutions: Norwegian University of Science and Technology, Chalmers University of Technology

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,150