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An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios

Authors :
Xiaofei Zhang
Qiaoya Wang
Jun Li
Xiaorong Gao
Bowen Li
Bingbing Nie
Jianqiang Wang
Ziyuan Zhou
Yingkai Yang
Hong Wang
Source :
Scientific Data, Vol 11, Iss 1, Pp 1-9 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract For highly autonomous vehicles, human does not need to operate continuously vehicles. The brain-computer interface system in autonomous vehicles will highly depend on the brain states of passengers rather than those of human drivers. It is a meaningful and vital choice to translate the mental activities of human beings, essentially playing the role of advanced sensors, into safe driving. Quantifying the driving risk cognition of passengers is a basic step toward this end. This study reports the creation of an fNIRS dataset focusing on the prefrontal cortex activity in fourteen types of highly automated driving scenarios. This dataset considers age, sex and driving experience factors and contains the data collected from an 8-channel fNIRS device and the data of driving scenarios. The dataset provides data support for distinguishing the driving risk in highly automated driving scenarios via brain-computer interface systems, and it also provides the possibility of preventing potential hazards in some scenarios, in which risk remains at a high value for an extended period, before hazard occurs.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.6ce7d59ea62744b58f161cf1b24dfd42
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-024-03353-6