Year: 2025
Type: article
Abstract: This paper focuses on the adversarial attack of autonomous vehicle. Based on robust optimization theory, an integrated defense architecture is proposed that includes dynamic course adversarial training (DCAT) and Bayesian uncertainty detector (BUD). The defense strategies are divided into three categories: model enhancement, input purification, and runtime monitoring, and their implementation methods and applicable stages are clarified. The results show that the DCAT+BUD method has high robust accuracy in white box, black box and physical attack scenarios, and effectively improves the safety of the auto drive system.
Author: Jianqiao Jiang
Institution: The University of Queensland
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414478023
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