Little Road Driving HUD: Heads-Up Display Complexity Influences Drivers’ Perceptions of Automated Vehicles

Document Title

Little Road Driving HUD: Heads-Up Display Complexity Influences Drivers’ Perceptions of Automated Vehicles

Document Information

• Subject Area: Human-Computer Interaction Design, Augmented Reality, Vehicle Automation
• Keywords: Augmented Reality, Heads-Up Display, Situational Awareness, Interaction Design, Vehicle Interface, User Interface

Research Background and Issues

• Identified Problems or Challenges:

  • There is ongoing debate about whether the complex information provided by current vehicle Heads-Up Displays (HUDs) helps drivers enhance situational awareness.
  • The increased visual complexity of HUDs may lead to difficulties in extracting information and interfere with drivers' primary task attention.
  • Drivers' perceptions of HUDs may vary depending on driving styles, the complexity of driving scenarios, and other factors.

• Importance of the Research:

  • As automated driving technology becomes increasingly prevalent, there is a lack of interface designs that enhance drivers' understanding of automated driving decisions.
  • HUDs are critical tools for improving drivers' perception of vehicles, decision transparency, and providing real-time information.

• Motivation and Related Work:

  • Existing literature lacks sufficient research on HUD complexity, driving styles, and their influence on human-vehicle interaction.
  • Previous studies have primarily explored the number of symbols or visual complexity of scenarios in HUDs but have not systematically analyzed HUD complexity across different contexts and driving styles.

Solution

• Proposed Method or Solution:

  • Design and test three augmented reality-based HUD complexity conditions (no HUD, minimal HUD, and complex HUD).
  • Conduct an online video experiment (N=298) to investigate the impact of HUD visualization complexity on drivers' situational awareness and perceptions.

• Innovative Aspects:

  • Introduced a multidimensional analysis approach to explore the effects of HUD visualization complexity, considering not only symbol classification but also the number of scene elements and the proportion of HUD pixel illumination.
  • Systematically studied the interaction effects between driving styles (e.g., anxious, patient) and HUD complexity in various driving scenarios.

• Implementation Steps and Key Techniques:

  1. Use two video scenarios (a static traffic light waiting scene and a dynamic urban driving scene) to showcase different Hudson HUD visualization designs.
  2. Analyze participants' driving styles using the academically recognized "Multidimensional Driving Style Inventory" (MDSI) questionnaire.
  3. Collect situational awareness test data and subjective feedback on HUD perceptions from participants.

Research Findings

• Specific Results:

  1. Scenario complexity significantly affects drivers' situational awareness, with higher scores observed in static scenarios compared to dynamic ones.
  2. Under HUD conditions, situational awareness scores were lower for both minimal and complex HUDs compared to the no HUD condition.
  3. Driving styles (e.g., anxious, adventurous) interact with HUD complexity and scenario characteristics to influence situational awareness, revealing various significant interaction effects.

• Comparative Advantages:

  • Compared to existing studies, this research identifies HUD complexity as a multidimensional result rather than a single dimension (e.g., type of elements).
  • By incorporating driving styles as a key dimension, the study proposes a new direction for HUD design to cater to personalized needs of different drivers.

• Experimental or Evaluation Results:

  1. Situational awareness scores: Minimal and complex HUDs showed no significant differences but were significantly lower compared to the no HUD condition.
  2. Subjective perception: Complex HUDs were perceived as more helpful than minimal ones, though no significant differences were observed in actual situational awareness improvement.

• Limitations and Future Directions:

  1. Limitations:
     • The ecological validity of video experiments is limited, as they cannot fully simulate real driving environments.
     • Situational awareness tests rely on recall data, which may differ from immediate reactions.
  2. Future Research Directions:
     • Validate the effects of HUDs in driving simulators or real driving scenarios.
     • Explore adaptive HUD designs that adjust complexity based on driving styles and scenario characteristics.
     • Investigate the impact of time-sensitive information displays on drivers' situational awareness.