Deep learning-based autonomous driving systems: a survey of attacks and defenses

Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning. However, autonomous driving systems are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyber attacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize the autonomous driving systems, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the autonomous driving systems workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers.

Original languageEnglish
Pages (from-to)7897-7912
Number of pages16
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number12
Early online date6 Apr 2021
DOIs
Publication statusE-pub ahead of print - 6 Apr 2021

Keywords

  • adversarial attacks
  • Autonomous driving
  • Autonomous vehicles
  • cyberattacks
  • Data models
  • deep learning
  • Deep learning
  • defenses
  • Laser radar
  • Safety
  • Sensors
  • Task analysis

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