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Think Aloud Protocol Applied in Naturalistic Driving for Driving Rules Generation.

Borja Monsalve1, Nourdine Aliane2, Enrique Puertas1

  • 1Science, Computing and Technology Department, Universidad Europea de Madrid, 28670 Madrid, Spain.

Sensors (Basel, Switzerland)
|December 8, 2020
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Summary
This summary is machine-generated.

The Think Aloud Protocol effectively models driver behavior in naturalistic driving scenarios. This method allows for the derivation of driving rules, aiding autonomous driving development.

Keywords:
autonomous drivingcognitive processdriver behaviornaturalistic drivingrule generationthink aloud protocol

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Area of Science:

  • Human-Computer Interaction
  • Autonomous Driving Systems
  • Cognitive Psychology

Background:

  • Modeling driver behavior is crucial for advancing autonomous driving technology.
  • Existing methods for driver behavior modeling have limitations in complex scenarios.
  • Naturalistic driving studies provide realistic data for understanding human driving.

Purpose of the Study:

  • To present the Think Aloud Protocol as a methodology for modeling driver behavior.
  • To demonstrate the application and validity of the Think Aloud Protocol in naturalistic driving.
  • To derive driving rules from driver verbalizations in complex scenarios, specifically roundabouts.

Main Methods:

  • The Think Aloud Protocol involves drivers verbalizing their thoughts while driving.
  • Data collection includes recording audio and video of drivers in naturalistic settings.
  • Analysis involves processing recordings to generate coded transcript files and derive driving rules.

Main Results:

  • The Think Aloud Protocol is applicable to naturalistic driving research.
  • The methodology facilitates the derivation of specific driving rules, as demonstrated in roundabout scenarios.
  • The study identified potential limitations of the Think Aloud Protocol in this context.

Conclusions:

  • The Think Aloud Protocol offers a viable and relatively easy approach to derive driving rules from naturalistic driving data.
  • This methodology contributes to a deeper understanding of driver behavior, essential for safe autonomous systems.
  • Further research can refine the protocol to address identified limitations and enhance its application.