Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis

被引:4
|
作者
Coyne R. [1 ]
Ryan L. [1 ]
Moustafa M. [2 ]
Smeaton A.F. [3 ]
Corcoran P. [4 ]
Walsh J.C. [1 ]
机构
[1] School of Psychology, University of Galway
[2] School of Engineering, University of Galway
[3] School of Computing, Dublin City University, Dublin
[4] Department of Electrical and Electronic Engineering, University of Galway
来源
关键词
Automated driving; Conditional automation; Human-automation interaction; Non-driving-related tasks; Psychophysiological measures; Transition of control;
D O I
10.1016/j.aap.2023.107243
中图分类号
学科分类号
摘要
In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers’ physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology. © 2023 The Author(s)
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