Study on driver’s braking intention identification based on functional near-infrared spectroscopy
Purpose - Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving dec...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2019-02-01
|
| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.emeraldinsight.com/doi/pdfplus/10.1108/JICV-09-2018-0007 |
| _version_ | 1797332890379354112 |
|---|---|
| author | Lei Zhu Shuguang Li Yaohua Li Min Wang Yanyu Li Jin Yao |
| author_facet | Lei Zhu Shuguang Li Yaohua Li Min Wang Yanyu Li Jin Yao |
| author_sort | Lei Zhu |
| collection | DOAJ |
| description | Purpose - Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions. The purpose of this study is to establish a driver intention prediction model. Design/methodology/approach - The authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention. The experiment was carried out in a virtual reality environment. During the experiment, the driving simulator recorded the driving data and the functional near-infrared spectroscopy (fNIRS) device recorded the changes in hemoglobin concentration in the cerebral cortex. After the experiment, the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network. Findings - The research results showed that the accuracy of the model established in this paper was 80.39 per cent. And, the model could identify the driver’s braking intent prior to his braking operation. Research limitations/implications - The limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic. At the same time, other actions of the driver were not taken into account when establishing the braking intention recognition model. Besides, the verification results obtained in this paper could only reflect the results of a few drivers’ identification of braking intention. Practical implications - This study can be used as a reference for future research on driving intention through fNIRS, and it also has a positive effect on the research of brain-controlled driving. At the same time, it has developed new frontiers for intention recognition of cooperative driving. Social implications - This study explores new directions for future brain-controlled driving and wheelchairs. Originality/value - The driver’s driving intention was predicted through the fNIRS device for the first time. |
| first_indexed | 2024-03-08T07:54:53Z |
| format | Article |
| id | doaj.art-90a9a82a747a4cccb3d7ad3d52cbc73a |
| institution | Directory Open Access Journal |
| issn | 2399-9802 |
| language | English |
| last_indexed | 2024-03-08T07:54:53Z |
| publishDate | 2019-02-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj.art-90a9a82a747a4cccb3d7ad3d52cbc73a2024-02-02T14:04:07ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022019-02-011310711310.1108/JICV-09-2018-0007619596Study on driver’s braking intention identification based on functional near-infrared spectroscopyLei Zhu0Shuguang Li1Yaohua Li2Min Wang3Yanyu Li4Jin Yao5School of Manufacturing Science and Engineering, Sichuan University, Chengdu, ChinaSchool of Manufacturing Science and Engineering, Sichuan University, Chengdu, ChinaDepartment of Neurology, Chengdu City First People’s Hospital, Chengdu, ChinaSchool of Manufacturing Science and Engineering, Sichuan University, Chengdu, ChinaSichuan University, Chengdu, ChinaSchool of Manufacturing Science and Engineering, Sichuan University, Chengdu, ChinaPurpose - Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together. One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions. The purpose of this study is to establish a driver intention prediction model. Design/methodology/approach - The authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention. The experiment was carried out in a virtual reality environment. During the experiment, the driving simulator recorded the driving data and the functional near-infrared spectroscopy (fNIRS) device recorded the changes in hemoglobin concentration in the cerebral cortex. After the experiment, the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network. Findings - The research results showed that the accuracy of the model established in this paper was 80.39 per cent. And, the model could identify the driver’s braking intent prior to his braking operation. Research limitations/implications - The limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic. At the same time, other actions of the driver were not taken into account when establishing the braking intention recognition model. Besides, the verification results obtained in this paper could only reflect the results of a few drivers’ identification of braking intention. Practical implications - This study can be used as a reference for future research on driving intention through fNIRS, and it also has a positive effect on the research of brain-controlled driving. At the same time, it has developed new frontiers for intention recognition of cooperative driving. Social implications - This study explores new directions for future brain-controlled driving and wheelchairs. Originality/value - The driver’s driving intention was predicted through the fNIRS device for the first time.https://www.emeraldinsight.com/doi/pdfplus/10.1108/JICV-09-2018-0007Machine learningfNIRSCooperative drivingDriving intention identification |
| spellingShingle | Lei Zhu Shuguang Li Yaohua Li Min Wang Yanyu Li Jin Yao Study on driver’s braking intention identification based on functional near-infrared spectroscopy Journal of Intelligent and Connected Vehicles Machine learning fNIRS Cooperative driving Driving intention identification |
| title | Study on driver’s braking intention identification based on functional near-infrared spectroscopy |
| title_full | Study on driver’s braking intention identification based on functional near-infrared spectroscopy |
| title_fullStr | Study on driver’s braking intention identification based on functional near-infrared spectroscopy |
| title_full_unstemmed | Study on driver’s braking intention identification based on functional near-infrared spectroscopy |
| title_short | Study on driver’s braking intention identification based on functional near-infrared spectroscopy |
| title_sort | study on driver s braking intention identification based on functional near infrared spectroscopy |
| topic | Machine learning fNIRS Cooperative driving Driving intention identification |
| url | https://www.emeraldinsight.com/doi/pdfplus/10.1108/JICV-09-2018-0007 |
| work_keys_str_mv | AT leizhu studyondriversbrakingintentionidentificationbasedonfunctionalnearinfraredspectroscopy AT shuguangli studyondriversbrakingintentionidentificationbasedonfunctionalnearinfraredspectroscopy AT yaohuali studyondriversbrakingintentionidentificationbasedonfunctionalnearinfraredspectroscopy AT minwang studyondriversbrakingintentionidentificationbasedonfunctionalnearinfraredspectroscopy AT yanyuli studyondriversbrakingintentionidentificationbasedonfunctionalnearinfraredspectroscopy AT jinyao studyondriversbrakingintentionidentificationbasedonfunctionalnearinfraredspectroscopy |