Abstract
Encounter risk prediction is critical for safe ship navigation, especially in congested waters, where ships sail very near to each other during various encounter situations. Prior studies on the risk of ship collisions were unable to address the uncertainty of the encounter process when ignoring the complex motions constituting the dynamic ship encounter behavior, which may seriously affect the risk prediction performance. To fill this gap, a novel AIS data-driven approach is proposed for ship encounter risk prediction by modeling intership behavior patterns. In particular, multidimensional features of intership behaviors are extracted from the AIS trace data to capture spatial dependencies between encountering ships. Then, the challenging task of risk prediction is to discover the complex and uncertain relationship between intership behaviors and future collision risk. To address this issue, we propose a deep learning framework. To represent the temporal dynamics of the encounter process, we use the sliding window technique to generate the sequences of behavioral features. The collision risk level at a future time is taken as the class label of the sequence. Then, the long short-term memory network, which has a strong ability to model temporal dependency and complex patterns, is extended to establish the relationship. The benefit of our approach is that it transforms the complex problem for risk prediction into a time series classification task, which makes collision risk prediction reliable and easier to implement. Experiments were conducted on a set of naturalistic data from various encounter scenarios in the South Channel of the Yangtze River Estuary. The results show that the proposed data-driven approach can predict future collision risk with high accuracy and efficiency. The approach is expected to be applied for the early prediction of encountering ships and as decision support to improve navigation safety.
Highlights
Water traffic has become increasingly busy with the rapid development of the shipping industry in recent years, which has led to an increased risk to individuals and society in terms of various aspects, especially ship-ship collision accidents
To associate the collision risk prediction with the evolution of the encounter process, we aim to model the relationship between the sequence of behavioral features and the future risk level
An automatic identification systems (AIS) data-driven approach has been derived for collision risk prediction in a vessel encounter situation by learning the intership behavior. e approach considers the relationship between intership behavior and future collision risk, which helps to predict the potential collision risk in various encounter situations in advance
Summary
Water traffic has become increasingly busy with the rapid development of the shipping industry in recent years, which has led to an increased risk to individuals and society in terms of various aspects, especially ship-ship collision accidents. Perceiving risk and predicting encounter situations between ships are crucial for the prevention of collision accidents, especially in busy traffic areas, where congested ships sail relatively close to each other [1]. To understand the risk level and take actions to decrease the possibility of collisions occurring in the waters, numerous efforts have been devoted to the risk analysis and assessment of ship collisions. Some focus on risk surveys among maritime experts and the conduct of qualitative collision risk analyses, primarily through empirical studies.
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