Driver Distraction From the EEG Perspective: A Review

Li, Guofa; Yuan, Yufei; Ouyang, Delin; Zhang, Long; Yuan, Bangwei; Chang, Xiaoyu; Guo, Zizheng; Guo, Gang

doi:10.1109/jsen.2023.3339727

Cited by 3 publications

(1 citation statement)

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“…Over recent years, different scientific works have been focused on distraction detection through the processing of neurophysiological data. In this regard, it has to be noted that also subjective and behavioral measurements were largely explored in the scientific literature for distraction evaluation [ 24 , 25 , 26 ]. However, such approaches are susceptible to bias and lack objectivity.…”

Section: Introductionmentioning

confidence: 99%

A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions

Ronca,

Brambati,

Napoletano

et al. 2024

Brain Sciences

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The drivers’ distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to “detect” eventual events, road traffic, and additional driving tasks that might contribute to the drivers’ distraction. Therefore, the present study aimed at developing an EEG-based “Distraction index” obtained by the combination of the driver’s mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver’s attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers’ distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers’ distraction (all p < 0.01).

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Section: Introductionmentioning

confidence: 99%