A Time-Efficient Approach for Decision-Making Style Recognition in Lane-Changing Behavior

Abstract

Fast recognition of a driver's decision-making style when changing lanes plays a pivotal role in a safety-oriented and personalized vehicle control system design. This article presents a time-efficient recognition method by integrating k-means clustering (k-MC) with the K-nearest neighbor (KNN) algorithm, called kMC-KNN. Mathematical morphology is implemented to automatically label the decision-making data into three styles (moderate, vague, and aggressive), while the integration of k-MC and the KNN algorithm helps to improve the recognition speed and accuracy. Our developed mathematical-morphology-based clustering algorithm is then validated by a comparison with agglomerative hierarchical clustering. Experimental results demonstrate that the developed kMC-KNN method, in comparison with the traditional KNN algorithm, can shorten the recognition time by more than 72.67% with a recognition accuracy of 90-98%. In addition, our developed kMCKNN method also outperforms a support vector machine in terms of recognition accuracy and stability. The developed time-efficient recognition approach would have great application potential for in-vehicle embedded solutions with restricted design specifications.