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Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Maritime Trajectories

Singh, Sandeep Kumar and Fowdur, Jaya Shradha and Gawlikowski, Jakob and Medina, Daniel (2022) Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Maritime Trajectories. IEEE Transactions on Intelligent Transportation Systems. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TITS.2022.3190834. ISSN 1524-9050.

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Official URL: https://ieeexplore.ieee.org/abstract/document/9839418


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 anomalous maritime 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.

Item URL in elib:https://elib.dlr.de/187933/
Document Type:Article
Title:Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Maritime Trajectories
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Medina, DanielUNSPECIFIEDhttps://orcid.org/0000-0002-1586-3269UNSPECIFIED
Date:July 2022
Journal or Publication Title:IEEE Transactions on Intelligent Transportation Systems
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Anomaly Detection; Evidential Deep Learning; Regression; Classification; Clustering; Graph; Uncertainty
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - FuturePorts
Location: Neustrelitz
Institutes and Institutions:Institute of Communication and Navigation > Nautical Systems
Deposited By: Medina, Daniel
Deposited On:23 Aug 2022 13:35
Last Modified:26 Aug 2022 10:40

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