Effects of HUD Information Location on Drivers' Cognitive and EEG Responses

HUD information location influenced cognitive performance, self-reported responses, and EEG responses, showing a clear efficiency pattern: Bottom-Left → Bottom-Right → Top-Left → Top-Right.

Problem

① High Cognitive Load in Driving

  • Continuous Perception–Comprehension–Projection under limited cognitive resources
    Situation Awareness (SA) Theory

    Fig. 1. Situation Awareness (SA) Theory

  • Cognitive overload ⬆︎
    ⇒ inattentional blindness & delayed response ⬆︎
    ⇒ accident risk ⬆︎
    Multiple Resource Theory (MRT)

    Fig. 2. Multiple Resource Theory (MRT)

② Cognitive Interference from HUD Information

  • Expansion of Non-Driving Related Tasks & infotainment ⬆︎
    ⇒ visual information load ⬆︎
    ⇒ overlay-induced attentional competition (HUD vs. environment)
    ⇒ inefficient resource allocation
    Perceptual Relationship between HUD Information and the Driver–Vehicle–Environment (DVE) System

    Fig. 3. Perceptual Relationship between HUD Information and the Driver–Vehicle–Environment (DVE) System

    Types of HUD and Their Projection Characteristics

    Fig. 4. Types of HUD and Their Projection Characteristics

③ Central Vision–Biased HUD Design

  • Central-focused HUD design
    ⇒ attentional shift cost ⬇︎
    ⇒ cognitive tunneling ⬇︎
    ⇒ safety & stable attention ⬆︎
    Field-of-View Angles (13°, 30°, 60°, 107°)

    Fig. 5. Field-of-View Angles (13°, 30°, 60°, 107°)

  • Limitation: Underutilization of Peripheral Vision
    Low-Utilization Areas in HUD Layout

    Fig. 6. Low-Utilization Areas in HUD Layout

  • Emerging Issue with AR-HUD: concentration of dynamic cues in central vision ⬆︎
    ⇒ cognitive tunneling ⬆︎
    ⇒ need for effective peripheral information distribution

④ Lack of Empirical Evidence on Peripheral HUD Locations

  • optimal peripheral HUD information location remains unclear
    HUD information location coordinates

    Fig. 7. HUD information location coordinates

  • Perception and cognitive processing differ across visual quadrants
    Four-quadrant model of the human visual field

    Fig. 8. Four-quadrant model of the human visual field

Approach

  • HUD locations in peripheral vision: Top-Left, Top-Right, Bottom-Left, Bottom-Right
  • Driving-simulation environment
  • Cognitive tasks: Stroop / Flanker
  • Multimodal measurements: performance, self-reported, and physiological metrics
Overview of Experimental Design

Fig. 5. Overview of Experimental Design

Stroop and Flanker Task Stimuli

Fig. 6. Stroop and Flanker Task Stimuli

Stimulus Positions and Response Buttons

Fig. 7. Stimulus Positions and Response Buttons

Experiment Structure and Trial Sequence Diagram

Fig. 8. Experiment Structure and Trial Sequence Diagram

Result

Cognitive performance significantly varied across HUD information locations. The bottom-left position showed the highest accuracy and fastest response times, while the top-right showed the lowest performance. EEG results indicated more efficient neural processing in lower visual field conditions compared to upper regions. Self-reported responses aligned with performance data, indicating lower perceived cognitive load in bottom positions.

Fig. 9. Experimental Scene

Efficiency Order of HUD Locations

Fig. 10. Efficiency Order of HUD Locations (Bottom-Left → Bottom-Right → Top-Left → Top-Right)

Insight

The observed performance differences indicate that cognitive processing in driving is shaped by inherent asymmetries in the human visual field. ERP results revealed earlier neural responses for stimuli presented in the bottom-left position, reflecting more efficient early-stage perceptual processing in this region. This suggests that the lower visual field—particularly the bottom-left—is optimal for processing task-relevant information during driving. Accordingly, HUD design should prioritize regions aligned with natural perceptual advantages rather than uniformly distributing information across the visual field.