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SOAR as a Cognitive Architecture for Modeling Driver Workload

SOAR as a Cognitive Architecture for Modeling Driver Workload. Randall Mauldin. Goal. To have onboard computer assistance that allows safe multi-tasking while driving. Reduction of accidents and unsafe driving due to the distraction of secondary tasks proves to be a cause worth pursuing.

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SOAR as a Cognitive Architecture for Modeling Driver Workload

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  1. SOAR as a Cognitive Architecture for Modeling Driver Workload Randall Mauldin

  2. Goal • To have onboard computer assistance that allows safe multi-tasking while driving. • Reduction of accidents and unsafe driving due to the distraction of secondary tasks proves to be a cause worth pursuing.

  3. Introduction • Develop a Computational Cognitive Model of the driving task to allow a safer and more efficient driving experience.

  4. How? • Develop a Cognitive Process Model (CPM) of a basic driver workload. • The CPM will take in to account various driver tasks and interpret their demand on cognition. • Develop computational specifications and implement them into a Cognitive Modeling Architecture.

  5. Possibilities for a CPM

  6. What is Driver Distraction? • Driver distraction lacks a precise, scientific definition. • Defined based upon four components: Impact, Agent, Mechanism, and Type.

  7. Impact and Agent • “A driver is delayed in the recognition of information necessary to safely maintain the lateral and longitudinal control of the vehicle (the driving task)” • “Due to some event, activity, object or person, within or outside the vehicle”

  8. Mechanism and Type • “That compels or tends to induce the driver’s shifting attention away from fundamental driving tasks” • “By compromising the driver’s auditory, biomechanical, cognitive or visual faculties, or combinations thereof”

  9. SOAR • State Operator and Result • Created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University in 1983. • The “state” is the situation that needs to be solved. • The “operator” is what changes the “state.”

  10. SOAR Soar’s 7 step decision cycle

  11. SOAR Structural model of Soar’s operation

  12. Key Features • Capable of representing large complex rule sets • Learns in a problem-solving context • New rules created for shorter sequences (“chunking”)

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