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

Source Organization:
University:Carnegie Mellon University
Principal Investigator:SeungJun Kim
PI Contact
Project Manager:Courtney Ehrlichman
Funding Source(s) and Amounts Provided (by each agency or organization):$80,000
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Start and End Dates:January 2016 - January 2017
Project Status:Active
Subject Categories:In-Vehicle Technologies
Abstract:Today’s technologies enable us to interact with information spaces ubiquitously – anytime and anywhere, including in cars while driving. These technologies deliver information proactively and allow drivers to maintain high situation awareness; however, the same technologies interrupt attention and cognition, and thereby increase workload and potentially hinder safe driving (e.g., push notifications may distract drivers who are looking at side mirrors). Nevertheless, existing research inadequately addresses how timing of information interventions and their presentation modes interact with the in-situ values and costs of the HCI experience in cars. To fill this gap, this project creates enabling technologies to coordinate contextual information with sensor-detected interruptible moments to adapt to drivers’ in-situ capabilities in attention and cognition. This project also presents a driver-centered workload manager that mediates interruptions and maintains a high quality HCI experience for drivers. The long-term goal of this project is to sustain and/or restore safe driving by reducing attentional and cognitive workload while delivering relevant information to drivers. The near-term goal is to refine our key technology, i.e., sensor-based detection of driver interruptibility, to create driver-experience assessment models based on information about real-time mechanisms that interact with drivers’ perceived value of presented information. This refined technology will underlie our new intelligent in-car workload manager. The project activities include the design of in-vehicle cyber-physical systems including a range of sensing and feedback prototypes; the development of visual analytics and machine-learning tools to assess driver interruptibility, drivers’ behavioral routines, and the quality of driver experience; and the conducting of human-subject experiments in naturalistic field driving situations as well as in a connected vehicle test bed (e.g., using Dedicated Short-Range Communication as an information stream).
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