The Advanced Human Factors Evaluation for Automotive Demand (AHEAD) consortium was founded in 2013 by DENSO, the MIT AgeLab, and Touchstone Evaluations, Inc. with the goal of creating an empirical HMI evaluation system based upon a scientifically valid framework that supports the assessment of demands placed on the driver by multimodal interfaces and other advanced technologies. Current members include Jaguar Land Rover, Google, Aptiv and Veoneer. AHEAD engages organizations across the automotive industry, participants have included leaders such as Honda, Subaru R&D, and Panasonic.
AHEAD's initial aims were to develop a robust method for measuring driver attentional demands that would:
1. Define an assessment space that not only considers the classic visual-auditory-cognitive-psychomotor (VACP) dimensions, but also considers spatial and temporal characteristics of a task.
2. Move away from defining acceptable demand based upon fixed limits for each type of demand, toward a framework in which demand could be optimized across dimensions (i.e. visual, auditory, haptic, vocal, manual, etc.) by taking into consideration the relative cost and benefit interactions of various input, output, and processing modalities.
3. Assess interactions between secondary tasks and the broader operating environment, as opposed to solely focusing on tasks themselves - while moving the language of assessment from driver distraction to emphasize driver attention management.
This approach enables AHEAD to consider (and begin to explicitly measure and model) driver focus. Its consideration of attention puts AHEAD in a position from which traditional HMI related attentional draws can be addressed, as well as the related concept of attention support that is central to many advanced automotive capabilities – such as attentional cuing, adaptive HMIs, active safety advisories, alerts, etc. Overall, this emphasis and approach enables AHEAD to strategically focus on developing a new scientifically supported method for HMI evaluation in today's relatively manually-controlled automobiles based on a foundation that will support future concepts (which will include increasing levels of automation) where HMI assessment will need to radically adapt to the changing characteristics of the driving situation.
AHEAD's initial work has led to the development of a safety grounded HMI evaluation approach that allows for the assessment of HMI demands across the time course of an interaction in the context of an attention management framework with strong ties to situational awareness and safety. The effort is also developing an early stage design (wireframe, etc.) demand estimation tool. The effort is rapidly moving to incorporate the consideration of underload, including that originating from automation, to deliver a comprehensive attention management focused methodology and supporting engineering tools for HMI evaluation and real-time management of driver attention.
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