Weather-Dependent Variations in Driver Gaze Behavior: A Case Study in Rainy Conditions

2025-08-31

AI Summary

This study investigates how experienced drivers adapt their visual behavior to mitigate safety risks arising from reduced visibility and diminished road friction during rainy conditions. Using eye-tracking technology, we compared the gaze behaviors of the same cohort of drivers navigating an identical highway segment under clear-weather and rainy conditions. A two-stage clustering approach combined with Markov transition matrices was developed to quantitatively identify rain-specific visual strategies: significantly increased glances toward the instrument cluster, prolonged average fixation duration, and elevated vertical gaze angle. Results indicate that rainy driving elevates cognitive load and triggers attentional reallocation, manifesting as a more conservative and focused visual monitoring pattern. These findings provide empirically grounded, quantitative evidence and a novel analytical paradigm for driver state recognition under adverse weather, enhanced Driver Monitoring Systems (DMS), and adaptive Advanced Driver Assistance Systems (ADAS) design.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Application Domains: Mobility, Driving & Flight

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Web data visualization

Abstract

Rainy weather significantly increases the risk of road accidents due to reduced visibility and vehicle traction. Understanding how experienced drivers adapt their visual perception through gaze behavior under such conditions is critical for designing robust driver monitoring systems (DMS) and for informing advanced driver assistance systems (ADAS). This case study investigates the eye gaze behavior of a driver operating the same highway route under both clear and rainy conditions. To this end, gaze behavior was analyzed by a two-step clustering approach: first, clustering gaze points within 10-second intervals, and then aggregating cluster centroids into meta-clusters. This, along with Markov transition matrices and metrics such as fixation duration, gaze elevation, and azimuth distributions, reveals meaningful behavioral shifts. While the overall gaze behavior focused on the road with occasional mirror checks remains consistent, rainy conditions lead to more frequent dashboard glances, longer fixation durations, and higher gaze elevation, indicating increased cognitive focus. These findings offer valuable insight into visual attention patterns under adverse conditions and highlight the potential of leveraging gaze modeling to aid in the design of more robust ADAS and DMS.

Problem

• Analyzing driver gaze behavior changes in rainy weather
• Investigating visual attention shifts for improved driver monitoring systems
• Modeling gaze patterns to enhance advanced driver assistance systems

Innovation

• Two-step clustering for gaze analysis
• Markov transition matrices modeling behavior
• Metrics: fixation duration, elevation, azimuth