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Updated: Sep 13, 2025
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
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Systematically modeling take-over performance: Considering the indirect effect of meteorological visibility mediated
Qingkun Li1, Zhenyuan Wang2, Wenjun Wang2
1Institute of Software Chinese Academy of Sciences, Beijing 100190, China.
Accident; Analysis and Prevention
|July 29, 2025
Summary
Drivers
Area of Science:
- Human-computer interaction
- Automotive engineering
- Cognitive psychology
Background:
- Conditionally automated driving systems require drivers to take over control.
- Driver take-over performance is influenced by numerous complex factors.
- Existing models lack comprehensive analysis across varying visibility and time constraints.
Purpose of the Study:
- To systematically investigate causal relationships between take-over time budget (TB), meteorological visibility, driver attention, and take-over performance.
- To develop a model for analyzing driver take-over performance under diverse conditions.
- To provide a theoretical basis for improving automated vehicle safety and user experience.
Main Methods:
- Structural equation modeling (SEM) to analyze complex causal relationships.
- Driving simulation experiment to collect data.
- Confirmatory factor analysis (CFA) to develop measurement models for attention and performance.
- Path analysis to reveal causal links.
Main Results:
- Meteorological visibility significantly affects reaction time and safety margin, mediated by driver attention.
- Driver attention and take-over time budget (TB) have substantial direct effects on take-over performance.
- Meteorological visibility has minimal impact on the control instability aspect of take-over performance.
Conclusions:
- Driver attention is a key mediator in the relationship between environmental conditions and take-over performance.
- Understanding these complex interactions is crucial for designing safer automated driving systems.
- The findings offer a theoretical framework for enhancing driver situational awareness and response in automated vehicles.
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