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