Abstract
Automobile intelligence improves the perception and decision-making capabilities of cars. This change of technology makes human and machine a joint cognitive and decision-making system, consequently changing the paradigm of human-machine interaction. The machine is no longer a mere tool but a team partner. Both academia and industry are actively investigating human-machine cooperative driving, such as take over, shared control and cooperative driving. However, currently, there is no cooperative driving framework that considers the cognitive characteristics of the interaction between humans and agents. We propose a cooperative interface interaction framework based on human-machine team cognitive information elements that need to be exchanged by both parties in cooperative driving, such as intention, situation awareness, prediction, and their impact on driving tasks. The proposed framework can provide a cognitive dimension for cooperative driving research, which can be used as a reference for the design of interaction in highly automated vehicles.
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Acknowledgements
This work was supported by The National Key Research and Development Program of China (No. 2018YFB1004903), National Social Science Fund (No. 19FYSB040), Shanghai Automotive Industry Science and Technology Development Foundation (No. 1717). Excellent Experimental Teaching Project of Tongji University and Graduate Education Research and Reform Project of Tongji University (No. 2020JC35).
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Zhang, J., Liu, Y., Hansen, P., Wang, J., You, F. (2021). Interactive Framework of Cooperative Interface for Collaborative Driving. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2021. Lecture Notes in Computer Science(), vol 12791. Springer, Cham. https://doi.org/10.1007/978-3-030-78358-7_24
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