Effect of Partially Automated Driving on Mental Workload, Visual Behavior and Engagement in Nondriving-Related Tasks: A Meta-Analysis.

IF 2.9 3区心理学 Q1 BEHAVIORAL SCIENCES

Human Factors Pub Date : 2025-03-03 DOI:10.1177/00187208251323132

Nicola Vasta, Francesco Biondi

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引用次数: 0

Abstract

Objective: The goal of this meta-analysis is to investigate the effect of partial automation on mental workload, visual behavior, and engagement in nondriving-related tasks.

Background: The literature on the human factors of operating partially automated driving offers mixed findings. While some studies show partial driving automation to result in suboptimal mental workload, others found it to impose similar levels of workload to the ones observed during manual driving. Likewise, while some studies evidence a marked increase in off-road glances when the automated system was engaged, other work has failed to replicate this pattern.

Method: 41 studies involving 1482 participants were analyzed using the PRISMA approach.

Results: No significant differences in mental workload were found between manual and partially automated driving, indicating no changes in mental workload between the two driving modes. A higher likelihood of glancing away from the forward roadway and engaging in nondriving-related tasks was found when the partially automated system was engaged.

Conclusion: Although the adoption of partial driving automation comes with some intended safety benefits, its use is also associated with an increased engagement in nondriving-related activities.

Application: These findings add to our understanding of the safety of partial automation and provide valuable information to Human Factors practitioners and regulators about the use and potential safety risks of using these systems in the real-world.

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来源期刊

Human Factors 管理科学-行为科学

CiteScore

10.60

自引率

6.10%

发文量

审稿时长

6-12 weeks

期刊介绍： Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.