Characterization of Driver Dynamic Visual Perception Under Different Road Linearity Conditions
Year: 2025
Type: article
Abstract: Drivers’ visual characteristics have an important impact on traffic safety, but existing studies are mostly limited to single-scene analyses and lack a systematic study on the dynamic changes in drivers’ eye tracking characteristics on different road sections. In this study, 23 drivers were recruited to wear the aSee Glasses eye tracking device and driving tests were conducted on four typical road sections, namely, straight ahead, turning, climbing, and downhill. The average fixation duration, pupil diameter, and the saccade amplitude of the eye tracking were collected, one-way analysis of variance (ANOVA) was used to explore the differences between the different road sections, and a mathematical model of changes in the visual characteristics over time was constructed, based on the fitting of the data. Computerized fitting models of changes over time were also constructed using the Origin 2021 software. The results show that different road sections had significant effects on drivers’ visual tasks: the longest average fixation duration was found in the straight road section, the largest pupil diameter was found in the curved road section, and the highest saccade amplitude was found in the downhill road section, reflecting the influence of the complexity of the driving task on the cognitive load. The fitted model further reveals the dynamic change law of eye tracking indicators over time, providing a quantitative basis for modeling driving behavior and visual tasks. This study provides a theoretical basis and practical reference for the optimal design of advanced driver assistance systems, traffic safety management, and road planning.
Source: Applied Sciences
DOI: 10.3390/app15116076
Authors: Zhenxiang Hao, Jianping Hu, Jin Ran, Xiaohui Sun, Yuhang Zheng, Chengzhang Li
Institution: Xinjiang University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410812894