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Driver readiness prediction: Bridging cognitive distraction monitoring and in-vehicle decision support systems
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- Authors
- Mi Chang, Eun Hye Jang, Woojin Kim, Daesub Yoon, Do Wook Kang
- Issue Date
- 2025-12
- Citation
- Decision Support Systems, v.199, pp.1-13
- ISSN
- 0167-9236
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- In Level 3 autonomous driving, drivers must quickly regain manual control when the vehicle exceeds its operational limits. Assessing driver readiness in real-time is crucial, especially under cognitive distraction, as delayed reactions can compromise safety. However, most vehicle systems rely on simple behavioral indicators, such as head movements from visual distractions, and struggle to predict driver readiness under complex cognitive distractions. Moreover, existing studies on cognitive distraction are primarily limited to laboratory settings or surveys, which limits their applicability to real-world driving conditions that require real-time decision making. To address these limitations, this study proposes an in-vehicle decision support system that analyzes cognitive distraction before take-over and predicts driver readiness in real-time. Phase 1 involved experiments with varying levels of cognitive distraction to collect data on driver behavior as well as psychological and physiological states to examine their relationship with driver readiness. Phase 2 used these findings to evaluate and compare deep learning models for predicting driver readiness. The results indicate that driver readiness can be predicted using eye-tracking data, with a model combining a transformer with a Random Forest Regressor achieving the best performance. This study enhances the understanding of the relationship between cognitive distraction and driver readiness. It applies these insights to an in-vehicle decision support system, improving the safety and reliability of autonomous vehicles. Furthermore, it provides a crucial foundation for advancing autonomous system design and driver monitoring technologies.
- KSP Keywords
- Autonomous System, Autonomous vehicle, Best performance, Driver Behavior, Driver monitoring, Driving conditions, In-vehicle, Model combining, Real-World driving, Safety and reliability, Take-over