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Sequential in-vehicle glance distributions: an alternative approach for analyzing glance data
Yusuke Yamani1, William J Horrey2, Yulan Liang2
1Old Dominion University, Norfolk, Virginia yyamani@odu.edu.
Human Factors
|May 16, 2015
Summary
Analyzing glance sequences in vehicles reveals that longer, later glances increase crash risk. Driver training significantly reduces problematic glances, improving safety by mitigating distraction from in-vehicle technology.
Area of Science:
- Human-Computer Interaction
- Transportation Safety
- Cognitive Psychology
Background:
- Increasing in-vehicle technologies create complex, visually demanding tasks for drivers.
- Extended glances away from the road increase the risk of motor vehicle crashes.
Purpose of the Study:
- To demonstrate the value of analyzing glance sequences and their distributions for understanding driver distraction.
- To investigate how glance duration changes across sequences during in-vehicle tasks.
Main Methods:
- Utilized eye-glance data from a distraction training program study.
- Examined changes in glance duration distributions across consecutive glances.
- Analyzed glance sequences during various in-vehicle tasks.
Main Results:
- Untrained drivers had nearly double the proportion of glances exceeding a 2-second threshold compared to trained drivers.
- Glances occurring third or later in a sequence were particularly associated with increased risk.
- Training effectively reduced the proportion of both early- and later-sequence glances exceeding safety thresholds.
Conclusions:
- Analyzing off-road glance duration in sequence provides insights into task-related distraction and training effectiveness.
- Sequential analysis of glance data aids researchers and practitioners in designing safer interfaces and training programs.
