Abstract
With the increase in vehicle automation, the role of the driver is shifting towards that of a partner or a teammate of the automated system. In this collaboration, the driver needs to monitor the environment and be a fallback operator of the automated system to ensure the performance and safety of driving, especially in the case of transition of control from the automated system to the human driver. To further our understanding the role of the direr (or user) in this context, we aim to conduct a scoping review that 1) synthesizes the cognitive and attentional factors that impact the performance of collaboration between the automated system and the human operator, and 2) identifies the different mechanisms and design elements that can potentially improve this performance and keep the driver in-the-loop. This work presents the theoretical grounding, the methodology, as well as the initial results of the review process.
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Tkiouat, Z., Labonté-LeMoyne, É., Titah, R., Saunier, N., Léger, PM., Sénécal, S. (2022). Attention and Human AI Collaboration - The Context of Automated Vehicles. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_89
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