Driver intention recognition based on error-related potentials

Through the collection and analysis of drivers' electroencephalogram (EEG) data, this study extracts characteristics of Error-related Potentials (ErrP) to real-time capture drivers' decision intentions. This enables rapid and accurate decision adjustments in emergency situations, establishing a closed-loop human-machine obstacle avoidance decision model. Three typical hazardous driving scenarios and ErrP experimental paradigms were designed. The integration of Prescan, Simulink, and Psychtoolbox was employed for joint simulation. EEG data from 15 subjects were collected and analyzed. Potential topography maps indicated that error events effectively elicited ErrP, containing error-related negativity (ERN) component around 150-200ms and error-related positivity (Pe) component around 400-450 ms after error event stimuli. Shrinkage Linear Discriminant Analysis (SKLDA), Linear Support Vector Machine (LSVM), and Probabilistic Kernel Support Vector Machine (PSVM) and fine-grained convolutional neural network (CNN) were used for ErrP classification. The average classification accuracy and area under curve (AUC) of the fine-grained CNN was (82.75±6.25)% and (85.48±4.46)% respectively, which demonstrated superior performance among the four algorithms. These results verify the feasibility of utilizing ErrP to infer drivers' intentions in emergency conditions.