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Related Experiment Videos

Tools for Transport: Driven to Learn With Connected Vehicles.

Nichole Morris1, Curtis Craig1, Jessica Hafetz Mirman2

  • 1Department of Mechanical Engineering, University of Minnesota.

Topics in Cognitive Science
|July 10, 2021
PubMed
Summary

Enhanced vehicle feedback can improve driver trust and performance in automated vehicles. This study explored how different feedback methods affect driver experience and reaction times in a virtual driving environment.

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Area of Science:

  • Human-Computer Interaction
  • Automotive Engineering
  • Cognitive Psychology

Background:

  • Vehicle automation aims to reduce traffic collisions by minimizing human error.
  • Automated driving systems vary in features, settings, and implementation, posing challenges for driver training.
  • Understanding driver interaction with these heterogeneous systems is crucial.

Purpose of the Study:

  • To investigate the influence of enhanced vehicle feedback on driver trust, effort, frustration, and performance.
  • To analyze driver reaction times in a virtual driving environment with varying feedback.
  • To contextualize findings within the literature on motor vehicle operation and skill development.

Main Methods:

  • An experimental study with 36 participants in a virtual driving environment.
Keywords:
ADASAutomated vehiclesCollision warningsLearning to driveMental modelsNovice driversTool connected vehiclesVehicle automation

Related Experiment Videos

  • Manipulation of enhanced vehicle feedback to assess its impact.
  • Measurement of driver trust, effort, frustration, and reaction time.

    • Main Results:

    • Enhanced vehicle feedback was found to influence driver trust, effort, and frustration.
    • Participant reaction times, indicating performance, were affected by the feedback conditions.
    • The study provides empirical data on the effects of feedback in automated driving.

    Conclusions:

    • Enhanced vehicle feedback is a significant factor in the human-machine interaction of automated vehicles.
    • Further research is needed to understand skill development and optimize driver training for increasingly automated automobiles.
    • Findings inform the design of more effective human-automated vehicle interfaces.