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Machine learning techniques, particularly those based on deep neural networks (DNNs), are widely adopted in the development of advanced driver-assistance systems (ADAS) and autonomous vehicles. While providing significant improvement over traditional methods in average performance, the usage of DNNs also presents great challenges to system safety, especially given the uncertainty of the surrounding environment, the disturbance to system operations, and the current lack of methodologies for predicting DNN behavior. In particular, adversarial attacks to the sensing input may cause errors in systems' perception of the environment and lead to system failure. However, existing works mainly focus on analyzing the impact of such attacks on the sensing and perception results and designing mitigation strategies accordingly. We argue that as system safety is ultimately determined by the actions it takes, it is essential to take an end-to-end approach and address adversarial attacks with the consideration of the entire ADAS or autonomous driving pipeline, from sensing and perception to planing, navigation and control. In this paper, we present our initial findings in quantitatively analyzing the impact of a type of adversarial attack (that leverages road patch) on system planning and control, and discuss some of the possible directions to systematically address such attack with an end-to-end view.

Abstract-With the rapid advancement of autonomous driving and vehicular communication technology, intelligent intersection management has shown great promise in improving transportation efficiency. In a typical intelligent intersection, an intersection manager communicates with autonomous vehicles wirelessly and schedules their crossing of the intersection. Previous system designs, however, do not address the possible communication delays due to network congestion or security attacks, and could lead to unsafe or deadlocked systems. In this work, we propose a delay-tolerant protocol for intelligent intersection management, and develop a modeling, simulation and verification framework for analyzing the protocol's safety, liveness and performance. Experiments demonstrate the advantages of our proposed protocol over traditional traffic light control, and more importantly, demonstrate the importance and effectiveness of using this framework to address timing (delay) in vehicular network applications. This work is the first step towards a comprehensive delayaware design and verification framework for practical vehicular network applications.

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