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

TLDR

Autonomous vehicles promise to reduce driver burden and traffic accidents, yet their rapid AI and IoT advances bring new security challenges that are the foremost concern. This article systematically reviews security threats to autonomous driving and offers guidelines to encourage secure design in academia and industry. The authors analyze threats across perception, navigation, and control, summarize defense strategies, and outline future research on deep‑learning‑based security issues.

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.