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

## TL;DR

This paper introduces a fast and accurate decision-making style recognition method for lane-change behavior by combining clustering and classification techniques, suitable for embedded vehicle systems.

## Contribution

It presents a novel time-efficient recognition approach integrating k-means clustering with KNN, enhanced by mathematical morphology for automatic data labeling.

## Key findings

- Recognition time reduced by over 72.67%.
- Achieved 90%-98% recognition accuracy.
- Outperformed SVM in accuracy and stability.

## Abstract

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.