Open AccessAnalysis of braking intention based on fNIRS in driving simulation experiments
Author(s) -
Zhu Lei,
Li Shuguang,
Li Yaohua,
Wang Min,
Zhang Chenyang,
Li Yanyu,
Yao Jin,
Ji Hao
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5304
Subject(s) - driving simulator , brake , perception , computer science , advanced driver assistance systems , cognition , identification (biology) , task (project management) , simulation , psychology , engineering , automotive engineering , artificial intelligence , neuroscience , botany , systems engineering , biology
Cooperative driving refers to a notion that advanced driver assistance system (ADAS) sharing the control with human driver and completing driving task together. One of the key technologies is that the ADAS can identify the driver's driving intention in real time to implement consistent driving decisions. Based on driving simulator (DS) and functional near‐infrared spectroscopy (fNIRS) experiments, this study established a model of driver's brake intention identification with machine‐learning algorithms and analysed cerebral cortex activity mechanism of the driver's brake intention with parametric test. This study suggested that the test accuracy of the model established here was 90.91%. Moreover, the activity in the Brodmann area 7 (BA7), BA17, and BA40 in cerebral cortex was significantly different between the driver with braking intention and those with driving at constant speed ( p < 0.05). The study presented here not only identified the driver's driving intention through fNIRS for the first time, but also analysed the brain activity of the drivers when he had the braking intention. It lays a foundation for the future research on the driving cognitive model about human perception and driver's driving intention identification.