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  1. Objective We investigated secondary–task–based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD. Background Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance decrement to be alleviated with secondary tasks due to increased task engagement. Method Participants watched a driving video simulating PAD and were required to identify hazardous vehicles throughout the 45-min drive. A total of 117 participants were assigned to three different vigilance-intervention conditions including a driving-related secondary task (DR) condition, a non-driving-related secondary task (NDR) condition, and a control condition with no secondary tasks. Results Overall, the vigilance decrement was shown over time, reflected in increased response times, reduced hazard detection rates, reduced response sensitivity, shifted response criterion, and subjective reports on task-induced stress. Compared to the DR and the control conditions, the NDR displayed a mitigated vigilance decrement. Conclusion This study provided convergent evidence for both resource depletion and disengagement as sources of the vigilance decrement. Application The practical implication is that infrequent and intermittent breaks using a non-driving related task may help alleviate the vigilance decrement in PAD systems. 
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  2. Automated Driving Systems (ADS), like many other systems people use today, depend on successful Artificial Intelligence (AI) for safe roadway operations. In ADS, an essential function completed by AI is the computer vision techniques for detecting roadway signs by vehicles. The AI, though, is not always reliable and sometimes requires the human’s intelligence to complete a task. For the human to collaborate with the AI, it is critical to understand the human’s perception of AI. In the present study, we investigated how human drivers perceive the AI’s capabilities in a driving context where a stop sign is compromised and how knowledge, experience, and trust related to AI play a role. We found that participants with more knowledge of AI tended to trust AI more, and those who reported more experience with AI had a greater understanding of AI. Participants correctly deduced that a maliciously manipulated stop sign would be more difficult for AI to identify. Nevertheless, participants still overestimated the AI’s ability to recognize the malicious stop sign. Our findings suggest that the public do not yet have a sufficiently accurate understanding of specific AI systems, which leads them to over-trust the AI in certain conditions.

     
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