Impact of touchscreen-based in-vehicle information systems operational complexity on driving behavior and mental workload in car following conditions

The interaction between the driver and the in-vehicle information system (IVIS) significantly impacts both the driver’s behavior and driving safety. This research aims to explore the influence of task complexity in touchscreen-based IVIS on driving behavior and mental workload. Based on the driving simulation, a test was designed for drivers to manually operate the IVIS under car-following conditions. Driving behavior and eye movement data were collected under different manual touchscreen task complexities. The effects of driver gender, driving experience, and the number of hands-on operations on driving behavior and mental workload were investigated by analysis of variance. A quantitative model of driver mental workload was constructed using exploratory factor analysis. The validity of this model was confirmed through the NASA-TLX scale. Additionally, a driver mental workload discrimination model was constructed using the Bayes discrimination method to achieve an accurate classification of mental workload. The results show that IVIS touchscreen operation tasks significantly affect driving behavior. The quantitative model, based on driving behavior and eye movement indicators, facilitates the evaluation of the driver’s mental workload under the influence of manual IVIS touchscreen operation tasks. Variables such as the driver’s gender, driving experience, and the number of hands-on operations significantly affect mental workload. The driver mental workload discrimination model correctly discriminated 87.0% of driver test samples. Cross-validation results showed an accuracy rate of 84.30%, indicating the model’s high precision in discriminating mental workload.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2025-01-28
DOI
https://doi.org/10.1177/09544070241310934
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
2.44
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article

Impact of touchscreen-based in-vehicle information systems operational complexity on driving behavior and mental workload in car following conditions

Tianzheng Wei, Ying Chen, Jianhao Wang, Haoxue Liu et al.
1 citations
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Human-Automation Interaction and Safety
2.44
article

Impact of touchscreen-based in-vehicle information systems operational complexity on driving behavior and mental workload in car following conditions

Tianzheng Wei, Ying Chen, Jianhao Wang, Haoxue Liu, Tong Zhu
article en
1 citations

Abstract

The interaction between the driver and the in-vehicle information system (IVIS) significantly impacts both the driver’s behavior and driving safety. This research aims to explore the influence of task complexity in touchscreen-based IVIS on driving behavior and mental workload. Based on the driving simulation, a test was designed for drivers to manually operate the IVIS under car-following conditions. Driving behavior and eye movement data were collected under different manual touchscreen task complexities. The effects of driver gender, driving experience, and the number of hands-on operations on driving behavior and mental workload were investigated by analysis of variance. A quantitative model of driver mental workload was constructed using exploratory factor analysis. The validity of this model was confirmed through the NASA-TLX scale. Additionally, a driver mental workload discrimination model was constructed using the Bayes discrimination method to achieve an accurate classification of mental workload. The results show that IVIS touchscreen operation tasks significantly affect driving behavior. The quantitative model, based on driving behavior and eye movement indicators, facilitates the evaluation of the driver’s mental workload under the influence of manual IVIS touchscreen operation tasks. Variables such as the driver’s gender, driving experience, and the number of hands-on operations significantly affect mental workload. The driver mental workload discrimination model correctly discriminated 87.0% of driver test samples. Cross-validation results showed an accuracy rate of 84.30%, indicating the model’s high precision in discriminating mental workload.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile EngineeringVol. 240(1)
Chang'an University (CN), Shandong Jiaotong University (CN)
Gender equality
Openalex Percentile: Top 8%
Human-Automation Interaction and Safety
2.44
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