Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures
K. T. Y. Mahima, Asanka G. Perera, Sreenatha G. Anavatti, Matthew Garratt
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.