Research on Driver Style Recognition Based on GA-K-Means and PSO-SVM

Abstract

<div>This article aims to address the challenge of recognizing driving styles, a task that has become increasingly complex due to the high dimensionality of driving data. To tackle this problem, a novel method for driver style clustering, which leverages the principal component analysis (PCA) for dimensionality reduction and an improved GA-K-means algorithm for clustering, is proposed. In order to distill low-dimensional features from the original dataset, PCA algorithm is employed for feature extraction and dimensionality reduction. Subsequently, an enhanced GA-K-means algorithm is utilized to cluster the extracted driving features. The incorporation of the genetic algorithm circumvents the issue of the model falling into local optima, thereby facilitating effective driver style recognition. The clustering results are evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and GAP value, demonstrating that this method yields more stable classification results compared to traditional clustering methods. In the final stage, a particle swarm optimization-SVM (PSO-SVM) algorithm is applied to classify the clustering results, which are then compared with results from other machine learning algorithms such as decision tree, naive Bayes network, and K-nearest-neighbor (KNN). This comprehensive approach to driver style recognition holds promise for enhancing traffic safety and efficiency. The accurate recognition of driving style can lay the foundation for further optimization of advanced driver assistance systems (ADAS).</div>