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Accurately detecting and identifying drivers' braking intention is the basisof man-machine driving. In this paper, we proposed an electroencephalographic(EEG)-based braking intention measurement strategy. We used the Car Learning toAct (Carla) platform to build the simulated driving environment. 11 subjectsparticipated in our study, and each subject drove a simulated vehicle tocomplete emergency braking and normal braking tasks. We compared the EEGtopographic maps in different braking situations and used three differentclassifiers to predict the subjects' braking intention through EEG signals. Theexperimental results showed that the average response time of subjects inemergency braking was 762 ms; emergency braking and no braking can be welldistinguished, while normal braking and no braking were not easy to beclassified; for the two different types of braking, emergency braking andnormal braking had obvious differences in EEG topographic maps, and theclassification results also showed that the two were highly distinguishable.This study provides a user-centered driver-assistance system and a goodframework to combine with advanced shared control algorithms, which has thepotential to be applied to achieve a more friendly interaction between thedriver and vehicle in real driving environment.

EEG-Based Detection of Braking Intention During Simulated Driving