The rapid development of artificial intelligence, especially deep learning
technology, has advanced autonomous driving systems (ADSs) by providing precise
control decisions to counterpart almost any driving event, spanning from
anti-fatigue safe driving to intelligent route planning. However, ADSs are
still plagued by increasing threats from different attacks, which could be
categorized into physical attacks, cyberattacks and learning-based adversarial
attacks. Inevitably, the safety and security of deep learning-based autonomous
driving are severely challenged by these attacks, from which the
countermeasures should be analyzed and studied comprehensively to mitigate all
potential risks. This survey provides a thorough analysis of different attacks
that may jeopardize ADSs, as well as the corresponding state-of-the-art defense
mechanisms. The analysis is unrolled by taking an in-depth overview of each
step in the ADS workflow, covering adversarial attacks for various deep
learning models and attacks in both physical and cyber context. Furthermore,
some promising research directions are suggested in order to improve deep
learning-based autonomous driving safety, including model robustness training,
model testing and verification, and anomaly detection based on cloud/edge
servers.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Deng_Y/0/1/0/all/0/1">Yao Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_T/0/1/0/all/0/1">Tiehua Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Lou_G/0/1/0/all/0/1">Guannan Lou</a>, <a href="http://arxiv.org/find/cs/1/au:+Zheng_X/0/1/0/all/0/1">Xi Zheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Jin_J/0/1/0/all/0/1">Jiong Jin</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_Q/0/1/0/all/0/1">Qing-Long Han</a>

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