Driver intention recognition based on error-related potentials

Yibo Wu; Bin Shen; Jun Zhan; Xiaorui Xiang

doi:10.1117/12.3049984

8 November 2024 Driver intention recognition based on error-related potentials

Yibo Wu, Bin Shen, Jun Zhan, Xiaorui Xiang

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163V (2024) https://doi.org/10.1117/12.3049984
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yibo Wu, Bin Shen, Jun Zhan, and Xiaorui Xiang "Driver intention recognition based on error-related potentials", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163V (8 November 2024); https://doi.org/10.1117/12.3049984

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