Location Anomalies Detection for Connected and Autonomous Vehicles

Xiaoyang Wang, Ioannis Mavromatis, Andrea Tassi, Raul Santos-Rodriguez, Robert Piechocki

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)
154 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-7281-3616-5
ISBN (Print)978-1-7281-3617-2
Publication statusPublished - 31 Oct 2019


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