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Location Anomalies Detection for Connected and Autonomous Vehicles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS)
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-7281-3616-5
ISBN (Print)978-1-7281-3617-2
DOIs
DateAccepted/In press - 1 Jun 2019
DatePublished (current) - 31 Oct 2019

Abstract

Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres will have access to suitably anonymized CAV mobility information. Safe and secure operations will then be contingent on early detection of anomalies. In this paper, a novel unsupervised learning model based on deep autoencoder is proposed to detect the self-reported location anomaly in CAVs, using vehicle locations and the Received Signal Strength Indicator (RSSI) as features. Quantitative experiments on simulation datasets show that the proposed approach is effective and robust in detecting self-reported location anomalies.

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/8887778. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 718 KB, PDF document

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