Sensor-based Assessment of the In-Situ Quality of Human-Computer Interaction in the Cars

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University:Carnegie Mellon University
Principal Investigator:SeungJun Kim
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Funding Source(s) and Amounts Provided (by each agency or organization): $81,316.00
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Project Status:Complete
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Abstract:Nowadays, technology enables us to interact with information anytime and anywhere, including in the car while driving. Appropriate in-vehicle interaction, however, depends on the current driving situation and the driver state. Intelligent information systems and advanced driver assistance systems help drivers maintain high situational awareness during vehicle operation, yet also increase visual distraction and cognitive load. Nevertheless, existing research inadequately addresses how an intervention of contextTECHNICAL PROPOSAL FORM sensitive information affects drivers’ attention management and cognitive load, and whether it increases workload and thus hinders attentive/safe driving. To fill this gap in human-vehicle interaction research, this project explores the design and development of a series of in-vehicle sensing prototypes, experiments with using Dedicated Short-Range Communication (DSRC) as an information stream, examines a broad range of sensor data streams to understand driver/driving states, and develops a model-based driver/driving assessment by using machine learning technology. The long-term goal of this project is to sustain and/or restore safe driving ability by reducing attentional and cognitive workload while delivering relevant information. The near-term goal is to understand driving situations and driver states, particularly when drivers engage in peripheral interactions—actions not related to the primary task of driving—as they indicate appropriate opportunities to interact with information during naturalistic driving.
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