Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories

TL;DR

This paper introduces a graph-based traffic pattern representation combined with evidential deep learning models to effectively detect maritime anomalous trajectories and unusual vessel maneuvers by leveraging uncertainty estimation.

Contribution

It proposes a novel graph-based trajectory representation and evidential deep learning approach for anomaly detection in maritime traffic, incorporating uncertainty estimation for improved accuracy.

Findings

Graph representation enhances deep learning models' ability to learn traffic patterns.

Evidential deep learning models effectively detect vessel anomalies using uncertainty estimates.

Uncertainty estimation improves detection of unusual vessel maneuvers and signal loss.

Abstract

Understanding and representing traffic patterns are key to detecting anomalous trajectories in the transportation domain. However, some trajectories can exhibit heterogeneous maneuvering characteristics despite confining to normal patterns. Thus, we propose a novel graph-based trajectory representation and association scheme for extraction and confederation of traffic movement patterns, such that data patterns and uncertainty can be learned by deep learning (DL) models. This paper proposes the usage of a recurrent neural network (RNN)-based evidential regression model, which can predict trajectory at future timesteps as well as estimate the data and model uncertainties associated, to detect maritime anomalous trajectories, such as unusual vessel maneuvering, using automatic identification system (AIS) data. Furthermore, we utilize evidential deep learning classifiers to detect unusual turns of vessels and the loss of transmitted signal using predicted class probabilities with associated uncertainties. Our experimental results suggest that the graphical representation of traffic patterns improves the ability of the DL models, such as evidential and Monte Carlo dropout, to learn the temporal-spatial correlation of data and associated uncertainties. Using different datasets and experiments, we demonstrate that the estimated prediction uncertainty yields fundamental information for the detection of traffic anomalies in the maritime and, possibly in other domains.