Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS
Abstract For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain–computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particular...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41549-9 |
| _version_ | 1797453338796621824 |
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| author | Hong Wang Xiaofei Zhang Jun Li Bowen Li Xiaorong Gao Zhenmao Hao Junwen Fu Ziyuan Zhou Mohamed Atia |
| author_facet | Hong Wang Xiaofei Zhang Jun Li Bowen Li Xiaorong Gao Zhenmao Hao Junwen Fu Ziyuan Zhou Mohamed Atia |
| author_sort | Hong Wang |
| collection | DOAJ |
| description | Abstract For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain–computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers’ mental activities in low-risk episode and high-risk episode were compared, the influences on passengers’ mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers’ driving risk cognition. |
| first_indexed | 2024-03-09T15:21:23Z |
| format | Article |
| id | doaj.art-598c5f956ae84daabb697f5b712cbca2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:21:23Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-598c5f956ae84daabb697f5b712cbca22023-11-26T12:48:17ZengNature PortfolioScientific Reports2045-23222023-09-0113111110.1038/s41598-023-41549-9Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRSHong Wang0Xiaofei Zhang1Jun Li2Bowen Li3Xiaorong Gao4Zhenmao Hao5Junwen Fu6Ziyuan Zhou7Mohamed Atia8School of Vehicle and Mobility, Tsinghua UniversitySchool of Vehicle and Mobility, Tsinghua UniversitySchool of Vehicle and Mobility, Tsinghua UniversitySchool of Medicine, Tsinghua UniversitySchool of Medicine, Tsinghua UniversitySchool of Computer Science, Carleton UniversityDepartment of Systems and Computer Engineering, Carleton UniversitySchool of Vehicle and Mobility, Tsinghua UniversityDepartment of Systems and Computer Engineering, Carleton UniversityAbstract For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain–computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers’ mental activities in low-risk episode and high-risk episode were compared, the influences on passengers’ mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers’ driving risk cognition.https://doi.org/10.1038/s41598-023-41549-9 |
| spellingShingle | Hong Wang Xiaofei Zhang Jun Li Bowen Li Xiaorong Gao Zhenmao Hao Junwen Fu Ziyuan Zhou Mohamed Atia Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS Scientific Reports |
| title | Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS |
| title_full | Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS |
| title_fullStr | Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS |
| title_full_unstemmed | Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS |
| title_short | Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS |
| title_sort | driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fnirs |
| url | https://doi.org/10.1038/s41598-023-41549-9 |
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