Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks

Abstract

The prototypes of autonomous vessels are expected to come into service within the coming years, but safety concerns remain due to complex traffic and natural conditions (e.g., Yangtze River). However, the response of autonomous vessels to potential accidents is still uncertain. The accident prevention for autonomous vessels is unconvincing due to the lack of objective studies on the causation analysis for maritime accidents. This paper constitutes an attempt to cover the aforementioned gap by studying the potential causations for maritime accidents in the Yangtze River by using a Bayesian-based network training approach. More than two hundred accidents reported between 2013 and 2019 in the Yangtze River are collected. As a result, a Bayesian network (BN) is successfully established to describe the causations among different risk influencing factors. By analysing the BN, this study reveals that the occurrence of maritime accidents (e.g., collision, grounding) can be expected to reduce with the development of autonomous vessels as the crews are removed. However, the extent of the consequences from some accidents (e.g., fire, critical weathers) could be more serious than conventional ones. Therefore, more attention and thoughts are needed to ensure the safe navigation of autonomous vessels in the Yangtze River.

Autonomous inland navigation: a literature review and extracontractual liability issues

“…An analysis of the potential risks is conducted by Zhao et al (2021), who look at the potential causes of accidents with a Bayesian-based network training approach: while autonomous vessels will improve safety as there will be crew, at the same time, some concerns arise on how an autonomous system will deal with other kinds of accidents (e.g. fire onboard).…”

A framework of marine collision risk identification strategy using AIS data

“…Zhao et al. (2021) used a data-driven Bayesian network based on accident data from the Yangtze River to determine the risk factors for marine accidents involving unmanned surface vessels (USVs). They built a network to determine how the future occurrence of maritime accidents, such as collisions, can be reduced as crews are removed from USVs, but accidents, such as fire and extreme weather, can be even worse.…”

Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques

“…In light of this, this paper aimed to build a BN model for the evolution of maritime accident scenarios using global maritime accident data. These data derived from the Global Integrated Shipping Information System (GISIS) and established by the International Maritime Organization (IMO) have been widely used by scholars in maritime accident studies [41][42][43][44][45][46][47]. The novelty of this research lies in the use of a BN-based approach to model maritime traffic accident scenarios.…”