Katherine S. Zhang (Purdue University), Claire Chen (Pennsylvania State University), Aiping Xiong (Pennsylvania State University)

Artificial intelligence (AI) systems in autonomous driving are vulnerable to a number of attacks, particularly the physical-world attacks that tamper with physical objects in the driving environment to cause AI errors. When AI systems fail or are about to fail, human drivers are required to take over vehicle control. To understand such human and AI collaboration, in this work, we examine 1) whether human drivers can detect these attacks, 2) how they project the consequent autonomous driving, 3) and what information they expect for safely taking over the vehicle control. We conducted an online survey on Prolific. Participants (N = 100) viewed benign and adversarial images of two physical-world attacks. We also presented videos of simulated driving for both attacks. Our results show that participants did not seem to be aware of the attacks. They overestimated the AI’s ability to detect the object in the dirty-road attack than in the stop-sign attack. Such overestimation was also evident when participants predicted AI’s ability in autonomous driving. We also found that participants expected different information (e.g., warnings and AI explanations) for safely taking over the control of autonomous driving.

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Jiayun Fu (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Bin B. Zhu (Microsoft Research Asia), Pingyi Hu (Huazhong University of Science and Technology), Ruixin Zhao (Huazhong University of Science and Technology), Yaru Jia (Huazhong University of Science and Technology), Peng Xu (Huazhong University of Science and Technology), Hai…

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