JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

A comprehensive survey of physical adversarial vulnerabilities in autonomous driving systems

Author(s): Shuai ZHAO, Boyuan ZHANG, Yucheng SHI, Yang ZHAI, Yahong HAN, Qinghua HU
Affiliation(s): College of Intelligence and Computing, Tianjin University, China; more
Corresponding email(s): yahong@tju.edu.cn
Key Words: Physical adversarial attacks; Physical adversarial defenses; AI safety; Deep learning; Autonomous driving system; Data-fusion; Adversarial vulnerability

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Shuai ZHAO, Boyuan ZHANG, Yucheng SHI, Yang ZHAI, Yahong HAN, Qinghua HU. A comprehensive survey of physical adversarial vulnerabilities in autonomous driving systems[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300867

@article{title="A comprehensive survey of physical adversarial vulnerabilities in autonomous driving systems",
author="Shuai ZHAO, Boyuan ZHANG, Yucheng SHI, Yang ZHAI, Yahong HAN, Qinghua HU",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2300867"
}

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%A Yucheng SHI
%A Yang ZHAI
%A Yahong HAN
%A Qinghua HU
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2300867"

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A1 - Yahong HAN
A1 - Qinghua HU
J0 - Frontiers of Information Technology & Electronic Engineering
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Autonomous driving systems (ADSs) have attracted wide attention in the machine learning communities. With the help of deep neural networks (DNNs), ADSs have shown both satisfactory performance under significant uncertainties in the environment and the ability to compensate for system failures without external intervention. However, the vulnerability of ADSs has raised concerns since DNNs have been proven vulnerable to adversarial attacks. In this paper, we present a comprehensive survey of current physical adversarial vulnerabilities in ADSs. We first divide the physical adversarial attack and defense methods by their restrictions of deployment into three scenarios: the real-world, the simulated, and the digital-world scenarios. Then, we consider the adversarial vulnerabilities that focus on various sensors in ADSs and separate them as camera-based, Light Detection And Ranging (LiDAR)-based, and multifusion-based attacks. Subsequently, we divide the attack tasks by traffic elements. For the physical defenses, we establish the taxonomy with reference to image preprocessing, adversarial detections, and model enhancement for the DNN models to achieve full coverage of the adversarial defenses. Based on the above survey, we finally discuss the challenges in this research field and provide further outlook on future directions.

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