Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey

TL;DR

This survey reviews recent advances in deep learning security for autonomous driving, highlighting potential threats, attack methods, and countermeasures to ensure safety and trustworthiness in autonomous systems.

Contribution

It provides a comprehensive overview of deep learning security issues in autonomous driving, including threat analysis, attack techniques, and future research directions.

Findings

Deep learning in autonomous driving faces significant security risks.

Recent attack algorithms pose threats to system safety.

Countermeasures are being developed to mitigate these risks.

Abstract

Since the 2004 DARPA Grand Challenge, the autonomous driving technology has witnessed nearly two decades of rapid development. Particularly, in recent years, with the application of new sensors and deep learning technologies extending to the autonomous field, the development of autonomous driving technology has continued to make breakthroughs. Thus, many carmakers and high-tech giants dedicated to research and system development of autonomous driving. However, as the foundation of autonomous driving, the deep learning technology faces many new security risks. The academic community has proposed deep learning countermeasures against the adversarial examples and AI backdoor, and has introduced them into the autonomous driving field for verification. Deep learning security matters to autonomous driving system security, and then matters to personal safety, which is an issue that deserves attention and research.This paper provides an summary of the concepts, developments and recent research in deep learning security technologies in autonomous driving. Firstly, we briefly introduce the deep learning framework and pipeline in the autonomous driving system, which mainly include the deep learning technologies and algorithms commonly used in this field. Moreover, we focus on the potential security threats of the deep learning based autonomous driving system in each functional layer in turn. We reviews the development of deep learning attack technologies to autonomous driving, investigates the State-of-the-Art algorithms, and reveals the potential risks. At last, we provides an outlook on deep learning security in the autonomous driving field and proposes recommendations for building a safe and trustworthy autonomous driving system.