Traffic injury prevention · 2026
The impact of roadside advertising density on driver visual attention and safety in Kuwait: An AI-driven analysis of simulator and eye-tracking data.
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Aiash Ahmad, Assad Ahmed, AlAhmad Mohamad, Alsarraf Hasan, Mazedi Sayed, AlMweel Ali, Ashkanani Janan, AlFadhli Nour, Robusté Francesc
Аннотация
OBJECTIVES: Driver distraction associated with roadside advertising remains a significant road safety concern, yet the precise physiological markers distinguishing stimulus-driven distraction from baseline driving are complex to isolate. This study quantified the impact of roadside advertising density on driver cognitive load and visual attention using a high-fidelity driving simulator and wearable eye-tracking technology.
METHODS: A cohort of 60 licensed drivers navigated simulated environments under three continuous conditions: high-density advertising, low-density advertising, and a baseline with no advertisements. Approximately 2.4 million continuous physiological samples including pupil diameter, blink density, and gaze velocity were analyzed using statistical variance testing and an ensemble of machine learning architectures.
RESULTS: The results demonstrated that roadside advertising density significantly impacts drivers' autonomic responses. The XGBoost algorithm proved to be the most robust classifier of driver cognitive states. Furthermore, feature importance analysis revealed that driver distraction is primarily driven by cognitive and autonomic metrics, such as mean pupil diameter and blink density, rather than mechanical shifts in visual scanning. Dense advertising is associated with acute cognitive stress. Moreover, spatially localized analysis reveals an 'autonomic visual rigidity,' characterized by transient, subconscious blink suppression. Finally, while the pupil effect was significant at the participant level (F, η2), blink suppression was non-significant globally (P=0.12) but distinctly present in the spatially stratified comparison (d,t).
CONCLUSIONS: The findings highlight the cognitive cost of dense visual clutter and advocate for the integration of cognitive load metrics into next-generation Advanced Driver Assistance Systems (ADAS). Furthermore, the data underscores the need to establish precise advertising safety thresholds, utilizing the 200 m spacing as a preliminary baseline for evaluating expanded zoning ordinances.
METHODS: A cohort of 60 licensed drivers navigated simulated environments under three continuous conditions: high-density advertising, low-density advertising, and a baseline with no advertisements. Approximately 2.4 million continuous physiological samples including pupil diameter, blink density, and gaze velocity were analyzed using statistical variance testing and an ensemble of machine learning architectures.
RESULTS: The results demonstrated that roadside advertising density significantly impacts drivers' autonomic responses. The XGBoost algorithm proved to be the most robust classifier of driver cognitive states. Furthermore, feature importance analysis revealed that driver distraction is primarily driven by cognitive and autonomic metrics, such as mean pupil diameter and blink density, rather than mechanical shifts in visual scanning. Dense advertising is associated with acute cognitive stress. Moreover, spatially localized analysis reveals an 'autonomic visual rigidity,' characterized by transient, subconscious blink suppression. Finally, while the pupil effect was significant at the participant level (F, η2), blink suppression was non-significant globally (P=0.12) but distinctly present in the spatially stratified comparison (d,t).
CONCLUSIONS: The findings highlight the cognitive cost of dense visual clutter and advocate for the integration of cognitive load metrics into next-generation Advanced Driver Assistance Systems (ADAS). Furthermore, the data underscores the need to establish precise advertising safety thresholds, utilizing the 200 m spacing as a preliminary baseline for evaluating expanded zoning ordinances.