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From Black Box to Transparency: The Impact of Multi-Level Visualization on User Trust in Autonomous Driving.

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Explainable visualizations in autonomous systems boost user trust and reduce cognitive load. Enhanced visualization significantly improved perceived usefulness and functional trust, demonstrating the value of meaningful transparency.

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Area of Science:

  • Human-computer interaction
  • Autonomous systems
  • Visualization

Background:

  • The
  • black-box
  • nature of autonomous systems hinders user trust and adoption.
  • Explainable visualizations are crucial for enhancing human-machine collaboration.

Purpose of the Study:

  • To investigate the impact of explainable visualizations on user trust and cognitive states in autonomous driving.
  • To compare user experience across different levels of system transparency.

Main Methods:

  • A within-subjects study with 29 participants performing high-fidelity driving tasks.
  • Three transparency conditions were evaluated: black-box, standard, and enhanced visualization.
  • Multimodal data analysis, including subjective ratings and physiological measures (pupil diameter, fixation duration).

Main Results:

  • Enhanced visualization significantly increased perceived usefulness (28.5%) and improved functional trust.
  • Average pupil diameter decreased by 15.3% with enhanced visualization, indicating lower cognitive load.
  • The black-box condition resulted in minimal engagement and the lowest trust ratings.

Conclusions:

  • Well-designed visualizations promote balanced trust calibration and cognitive efficiency.
  • "Meaningful transparency" is a key design principle for autonomous vehicle interfaces.
  • Transparency enhances user experience and system performance in autonomous systems.