Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task

Giorgi, Andrea; Borghini, Gianluca; Colaiuda, Francesca; Menicocci, Stefano; Ronca, Vincenzo; Vozzi, Alessia; Rossi, Dario; Aricò, Pietro; Capotorto, Rossella; Sportiello, Simone; Petrelli, Marco; Polidori, Carlo; Varga, Rodrigo; Van Gasteren, Marteyn; Babiloni, Fabio; Di Flumeri, Gianluca · 2024 · Crossref

DOI: 10.3390/bs14111090

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates the impact of driving fatigue onset on visual attention in professional drivers, addressing a critical gap in road safety research. While fatigue is a major contributor to accidents, existing literature predominantly focuses on severe fatigue states induced by long-duration protocols (90+ minutes). This approach fails to capture the early signs of fatigue, which are crucial for timely intervention. The authors aimed to determine if visual attention patterns change during the onset of fatigue and whether these changes differ between short-range van drivers and long-range truck drivers. The experimental design involved 19 professional drivers (9 van drivers in Italy, 10 truck drivers in Spain) participating in a simulated driving task. The protocol began with a 15-minute high-demand driving task on a racetrack to deplete mental resources, followed by a 45-minute monotonous driving task on a repetitive urban road. This sequence was designed to induce moderate fatigue rather than severe exhaustion. To objectively identify the onset of fatigue, the researchers utilized electroencephalography (EEG) data processed with the MDrow index, a validated metric for detecting drowsiness. This allowed for the precise labeling of "fatigued" spans at an individual level, rather than assuming fatigue based on time elapsed. Visual attention was monitored using eye-tracking technology, which recorded gaze distribution across areas of interest. Subjective fatigue and sleepiness were also assessed using the Chalder Fatigue Scale and the Karolinska Sleepiness Scale to validate the experimental conditions. The results demonstrated that the EEG-driven approach successfully detected the onset of fatigue during the simulated task. Eye-tracking analysis revealed that when drivers entered a fatigued state, their visual attention shifted toward non-informative portions of the driving environment, indicating a degradation in the allocation of visual resources. This suggests that fatigue impairs the driver's ability to focus on relevant stimuli necessary for safe driving. Furthermore, the study found no significant difference in the impact of fatigue on visual attention between the two groups of professional drivers. Despite differences in their typical work habits—van drivers accustomed to short-distance deliveries and truck drivers to long-haul driving—the onset of fatigue affected their visual attention patterns similarly. The significance of this research lies in its validation of an EEG-driven method for detecting early fatigue onset, offering a more precise alternative to traditional time-based assumptions. By linking objective neurophysiological markers to specific changes in visual behavior, the study provides evidence that fatigue alters how drivers scan their environment. These findings have implications for the development of driver monitoring systems and road infrastructure design. Understanding that fatigued drivers focus on non-informative areas can inform the creation of "nudge" strategies in road geometry or alert systems that target these specific attentional deficits, potentially reducing accidents caused by human error.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• drowsiness detection algorithms
• drowsiness
• gaze based attention detection
• vigilance
• truck driver fatigue
• time on task

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Methodological Resource: tool software, measurement protocol