EEG-Based Emergency Braking Prediction Using Data Ablation and SVM Classification

Abstract

Contemporary advanced driver assistance system (ADAS) features for semi-autonomous vehicles include braking assistance during collision avoidance. Although pre-collision detection typically relies on sensing systems to enable production vehicles to perceive oncoming road obstacles, the physiological state of the driver is not measured to predict emergency braking. On the other hand, previous driving simulation experiments have demonstrated the ability of regularized linear discriminant analysis (RLDA) to predict pre-collision braking using brain signals from multiple electroencephalogram (EEG) electrodes. In contrast, the current study used EEG data from these previous experiments to determine the quality of support vector machine (SVM) predictions as a first step towards realizing a brain-computer interface for emergency braking. Power spectral density (PSD) features were extracted from the EEG of one electrode to train and evaluate an SVM. Through a novel data ablation analysis, the optimal number of PSD components was determined to optimize model classification quality measured by the area under the curve (AUC). A comparison of the proposed model to the previous RLDA and other machine learning methods indicated that the SVM had a superior AU C. Thus, the proposed model is a candidate for assisting ADASs with pre-collision detection. Moreover, since the proposed model only utilized one electrode, our study potentially contributes to the facilitation of brain-computer interfaces for autonomous vehicles.