The Impact of Physical Exercise on Driver Attention Reaction Time and Decision Making
This paper presents an advanced Attention-Driven Reaction Framework (ADRF) to model the dynamic interplay between driver attention and reaction under complex road conditions. Traditional methods often rely on static or linear models, failing to capture temporal dependencies and individual variations...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10975813/ |
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| Summary: | This paper presents an advanced Attention-Driven Reaction Framework (ADRF) to model the dynamic interplay between driver attention and reaction under complex road conditions. Traditional methods often rely on static or linear models, failing to capture temporal dependencies and individual variations. ADRF integrates probabilistic attention modeling with dynamic systems to predict reaction behaviors with higher accuracy. To further enhance interpretability and adaptability, we propose the Reaction-Augmented Attention Optimization (RAAO) strategy, which employs a feedback-driven mechanism to refine attention allocation and reaction prediction. Experimental results show that ADRF improves reaction time prediction accuracy by 25% and decision-making accuracy by 30% compared to baseline models. These findings highlight the potential of ADRF in improving driver performance and reducing traffic accidents. These advancements offer a comprehensive approach to reducing road traffic accidents, aligning with multidisciplinary initiatives aimed at improving road safety. |
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| ISSN: | 2169-3536 |