The Security of Autonomous Driving: Threats, Defenses, and Future Directions

Kui Ren*, Qian Wang, Cong Wang, Zhan Qin, Xiaodong Lin

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

188 Citations (Scopus)

Abstract

Autonomous vehicles (AVs) have promised to drastically improve the convenience of driving by releasing the burden of drivers and reducing traffic accidents with more precise control. With the fast development of artificial intelligence and significant advancements of the Internet of Things technologies, we have witnessed the steady progress of autonomous driving over the recent years. As promising as it is, the march of autonomous driving technologies also faces new challenges, among which security is the top concern. In this article, we give a systematic study on the security threats surrounding autonomous driving, from the angles of perception, navigation, and control. In addition to the in-depth overview of these threats, we also summarize the corresponding defense strategies. Furthermore, we discuss future research directions about the new security threats, especially those related to deep-learning-based self-driving vehicles. By providing the security guidelines at this early stage, we aim to promote new techniques and designs related to AVs from both academia and industry and boost the development of secure autonomous driving.
Original languageEnglish
Pages (from-to)357-372
JournalProceedings of the IEEE
Volume108
Issue number2
Online published4 Nov 2019
DOIs
Publication statusPublished - Feb 2020

Research Keywords

  • Autonomous vehicles
  • Autonomous vehicles (AVs)
  • Global Positioning System
  • in-vehicle protocol
  • in-vehicle systems
  • Jamming
  • Laser radar
  • Security
  • security
  • Sensors
  • sensors.

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