Recent developments on connected and automated vehicles (CAV) show that many companies, such as Tesla, Lyft, and Waymo, are substantially investing in the development of perception modules based on deep learning algorithms. However, deep learning algorithms are susceptible to adversarial attacks aimed at modifying the input of the neural network to induce a misclassification, which may compromise vehicle decision-making and, therefore, functional safety. The overall vision of this research is to develop defense techniques capable of making CAVs more resilient to adversarial attacks and, thus, able to satisfy more stringent system safety and performance requirements.
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connected and automated vehicles, robust deep learning, vehicle safety and security
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