LAUSR

With the unprecedented development of deep learning, autonomous vehicles (AVs) have achieved tremendous progress nowadays. However, AV supported by DNN models is vulnerable to data poisoning attacks, hindering the large-scale application of autonomous driving. For example, by injecting carefully designed poisons into the training dataset of the DNN model in the traffic sign recognition system, the attacker can mislead the system to make targeted misclassification or cause a reduction in model classification accuracy. In this article, we conduct a thorough investigation of the state-of-the-art data poisoning attacks and defenses against AVs. According to whether the attacker needs to manipulate the data labeling process, we divide the state-of-the-art attack approaches into two categories, i.e., dirty-label attacks and clean-label attacks. We also differentiate the existing defense methods into two categories based on whether to modify the training data or the models, i.e., data-based defenses and model-based defenses. In addition to a detailed review of attacks and defenses in each category, we also give a qualitative comparison of the existing attacks and defenses. Besides, we provide a quantitative comparison of the existing attack and defense methods through experiments. Last but not least, we pinpoint several future directions for data poisoning attacks and defenses in AVs, providing possible ways for further research.