Hengyi Liang, Ruochen Jiao (Northwestern University), Takami Sato, Junjie Shen, Qi Alfred Chen (UC Irvine), and Qi Zhu (Northwestern University) Best Short Paper Award Winner!

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.

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