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Line-based and plane-based icon research for automotive user interfaces.

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Visual features of icons impact memory load, not recognition accuracy, in autonomous driving systems. Line-based icons suit low memory load, while plane-based icons excel in high memory load scenarios for better automotive user interface design.

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emergency situationsevent-related potentialshuman–machine interactionicon memoryicon recognitionuser interface

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Automotive Engineering

Background:

  • Icons are crucial for overcoming language barriers in human-machine interaction, particularly in autonomous driving.
  • Limited research exists on icon design principles for autonomous vehicles, with inconsistent classification methods and a focus on preference over cognitive mechanisms.
  • Cognitive load theory provides a framework for understanding how visual features of icons affect user performance.

Purpose of the Study:

  • To classify icons based on visual features relevant to autonomous driving interfaces.
  • To investigate the impact of these visual features on icon recognition and memory load.
  • To provide empirical evidence for optimizing icon design in automotive user interfaces.

Main Methods:

  • Utilized electroencephalography (EEG) in a rapid interaction experiment with 43 participants.
  • Collected data on icon recognition accuracy and memory load across different icon categories.
  • Classified icons based on distinct visual features (e.g., line-based vs. plane-based).

Main Results:

  • Visual features did not significantly affect icon recognition accuracy.
  • Visual features significantly influenced memory load.
  • Line-based icons were more effective under low memory load conditions.
  • Plane-based icons showed advantages in high memory load tasks.

Conclusions:

  • Icon design in autonomous driving should consider cognitive load, not just visual appeal or attention.
  • Tailoring icon visual features to expected memory load can enhance user experience and performance.
  • Findings offer practical guidance for designing effective automotive user interfaces.