Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors

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.

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

“…e current studies related to MASS collision avoidance can be categorized into two groups [9]. One aims to developing the alert systems for MASS to detect potential dangers of collision, using collision risk index [29][30][31], ship domain [32], dangerous region in velocity-space [33] and probability of collision [34], risk prediction based on deep learning [35], etc. Different from the manned ship, objects of CAS for MASS are conflict detections, which are difficult in the perception of collision situations [9].…”

On the performance evaluation of object classification models in low altitude aerial data

“…The applications of unmanned aerial vehicles (UAVs) include autonomous driving cars [ 1 ], object detection and classification [ 2 ], spotting violent crowd behaviors [ 3 ], traffic monitoring [ 4 ], and aerial terrain analysis [ 5 ]. Low-altitude aerial images retrieved from UAVs incorporate public safety in vehicle accidents [ 6 ], ship collisions [ 7 ], border-power lines [ 8 ], crowd surveillance [ 9 ], and energy inspection from solar farms [ 10 ]. Low-altitude aerial images in urban settings have different features than remote sensing or standard datasets.…”