PUBLICATIONS

Identifying Research Gaps through Self-Driving Car Data Analysis

M.L. Cummings1, Senior Member, IEEE and Ben Bauchwitz2

As machines increasingly behave more like active cognitive agents than passive tools, additional heuristics for supporting joint human-machine activity are urgently needed to complement existing usability heuristics. Despite the rich and extensive design guidance produced by forty years of cognitive systems engineering (CSE) and related fields, the lack of large-scale impact can be attributed, in part, to insufficient translation of CSE principles and guidelines to language and tools that are ready for designers and other decision-makers responsible for these automation-infused solutions. Towards this need, we synthesized a partial and preliminary list of ten machine requirements intended to capture some of the essentials of joint activity. We believe solidifying these essentials and their implications for machines is a first and necessary step towards deriving joint activity design heuristics that are valuable, practical, and sustainable for operational personnel. Through iterative refinement, we believe the combination of strong ideas and strong practicality in these tools can be the basis for a large-scale shift in the design and evaluation of human-machine teams.

1 Duke University Institute of Brain Sciences
2 Charles River Analytics, Inc.

For More Information

To learn more contact Ben Bauchwitz. Published in IEEE Transactions on Intelligent Vehicles or Research Gate.

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