Malicious user detection for cooperative mobility tracking in autonomous driving

Wang Pi, Pengtao Yang, Dongliang Duan, Chen Chen, Xiang Cheng, Liuqing Yang, Hang Li

Research output: Contribution to journalArticlepeer-review

Abstract

The mobility status of self and surrounding vehicles provides important information to various tasks in autonomous driving (AD) and intelligent transportation system (ITS). Accordingly, a precise, stable, and robust mobility tracking framework is essential. Compared with self-tracking that relies only on mobility observations from onboard sensors [e.g., global positioning system (GPS), inertial measurement unit (IMU), and camera], cooperative tracking markedly increases the precision and reliability of the mobility information by integrating observations from roadside units (RSUs) and nearby vehicles through vehicle-to-everything (V2X) communications in the Internet of Vehicles (IoV). Nevertheless, cooperative tracking can be quite vulnerable if there are malicious users sending bogus observations in the cooperative network. In this article, we present a malicious user detection framework, which includes two sequential detection algorithms and a secure mobility data exchange and fusion model to detect and remove bogus mobility information and integrate proposed detection algorithms with previous data fusion algorithms, which secures the cooperative mobility tracking in AD, ITS. Simulations validate the effectiveness and robustness of the proposed framework under different types of attacks.

Original languageEnglish (US)
Article number8998259
Pages (from-to)4922-4936
Number of pages15
JournalIEEE Internet of Things Journal
Volume7
Issue number6
DOIs
StatePublished - Jun 2020
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received September 9, 2019; revised January 22, 2020; accepted January 31, 2020. Date of publication February 13, 2020; date of current version June 12, 2020. This work was supported in part by the Ministry National Key Research and Development Project under Grant 2017YFE0121400, in part by the Guangdong Key Research and Development Project under Grant 2019B010153003, in part by the National Science Foundation under Grant CNS-1932413 and Grant CNS-1932139, and in part by the Open Research Fund from Shenzhen Research Institute of Big Data under Grant 2019ORF01006. (Corresponding author: Xiang Cheng.) Wang Pi, Pengtao Yang, Chen Chen, and Xiang Cheng are with the State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China (e-mail: piwang@pku.edu.cn; ypt@pku.edu.cn; c.chen@pku.edu.cn; xiangcheng@pku.edu.cn).

Keywords

  • Autonomous driving (AD)
  • Cooperative mobility tracking
  • Intelligent transportation system (ITS)
  • Internet of Vehicles (IoV)
  • Malicious user detection
  • Sequential detection

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