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The correlation between drivers' road familiarity and glance behavior using real vehicle experimental data and
Jing Huang1, Yezi Hu1, Lin Hu2,3
1College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
Traffic Injury Prevention
|May 6, 2024
Summary
Driver road familiarity significantly impacts glance behavior. This study developed an 83% accurate model to evaluate driver familiarity using eye-tracking data, enhancing driving safety.
Area of Science:
- Human-Computer Interaction
- Transportation Psychology
- Cognitive Science
Background:
- Road familiarity is crucial for driver visual behavior and driving safety.
- Limited research quantifies the relationship between road familiarity and visual features in complex environments.
- Urban intersections present unique challenges for drivers due to complex visual information.
Purpose of the Study:
- To explore the correlation between drivers' glance behavior and road familiarity at urban intersections.
- To develop a quantitative evaluation model for driver road familiarity based on glance behavior.
- To improve driving safety through a better understanding of driver familiarity.
Main Methods:
- Conducted real vehicle experiments with 24 drivers of varying road familiarity.
- Recorded eye movement data and divided the visual field into 10 areas of interest (AOIs).
- Extracted measures: average glance duration, number of glances, and fixation transition probabilities; employed factor analysis for model construction.
Main Results:
- Significant differences in glance duration and fixation transitions were observed between familiar and unfamiliar drivers.
- Specific AOIs (forward, left window, left rearview mirror, left forward) and transitions showed notable variations.
- The developed driver road familiarity evaluation model achieved an accuracy rate of 83%.
Conclusions:
- A strong correlation exists between driver road familiarity and glance behavior.
- Road familiarity can be integrated into driver working status assessment for improved safety.
- Findings offer valuable insights for designing advanced driving assistance systems and reducing driver workload.
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