Adversarial Examples in Environment Perception for Automated Driving (Review)
It addresses the problem of adversarial vulnerabilities in automated driving systems for researchers and practitioners, but it is incremental as a review paper.
This survey reviews the development of adversarial robustness research over the past decade, focusing on attack and defense methods and their applications in automated driving, highlighting the risks posed by imperceptible perturbations to deep neural networks.
The renaissance of deep learning has led to the massive development of automated driving. However, deep neural networks are vulnerable to adversarial examples. The perturbations of adversarial examples are imperceptible to human eyes but can lead to the false predictions of neural networks. It poses a huge risk to artificial intelligence (AI) applications for automated driving. This survey systematically reviews the development of adversarial robustness research over the past decade, including the attack and defense methods and their applications in automated driving. The growth of automated driving pushes forward the realization of trustworthy AI applications. This review lists significant references in the research history of adversarial examples.