Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures

Article

Yasas, K. T., Perera, Asanka G., Anavatti, Sreenatha and Garratt, Matt. 2024. "Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures." IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2024.3456293
Article Title

Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures

ERA Journal ID3746
Article CategoryArticle
AuthorsYasas, K. T., Perera, Asanka G., Anavatti, Sreenatha and Garratt, Matt
Journal TitleIEEE Transactions on Intelligent Transportation Systems
Number of Pages27
Year2024
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1524-9050
1558-0016
Digital Object Identifier (DOI)https://doi.org/10.1109/TITS.2024.3456293
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/10682109
Abstract

At present the perception system of autonomous vehicles is grounded on 3D vision technologies along with deep learning to process depth information. Although deep learning models for 3D perception give promising results, recent research demonstrates that they are also vulnerable to adversarial attacks similar to deep learning models trained on 2D images. As a result, it is essential to further explore the vulnerabilities of 3D perception models in autonomous vehicles and find methods to cope with the risks associated with these adversarial vulnerabilities, in order to improve the social acceptance of commercial autonomous vehicles. This study aims to provide an in-depth overview of the recent adversarial attacks and countermeasures against 3D perception models on autonomous vehicles. Further, challenges associated with the research domain and future research directions are highlighted to make autonomous vehicles robust against adversarial attacks.

KeywordsAdversarial attack; autonomous vehicles; 3D perception; deep learning; LiDAR
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204007. Control engineering, mechatronics and robotics
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Byline AffiliationsUniversity of New South Wales
School of Engineering
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