Braking intensity recognition with optimal K-means clustering algorithm

Abstract

Recognizing a driver’s braking intensity plays a pivotal role in the development of modern driver assistance and energy management systems; and therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a system for recognizing a driver’s braking intensity based on the pressure produced in the brake master cylinder. In this regard, a model-based, synthetic data generation concept is used to generate the training dataset. This technique involves two closed-loop controlled models: an upper-level longitudinal vehicle dynamics model, and a lower-level brake hydraulic, dynamic model. The adaptive particularly tunable fuzzy particle swarm optimization algorithm is recruited to solve the optimal K-means clustering problem and to determine the best number of clusters and centroids into which the brake pressure data are to be divided. The obtained results reveal that the brake pressure data for a vehicle traveling the new European driving cycle can be best partitioned into two clusters. A driver’s braking intensity may, therefore, be clustered as moderate or intensive. With the ability to automatically recognize a driver’s pedal feel, the system developed in this research could be implemented in intelligent driver assistance systems as well as in electric vehicles equipped with intelligent, electromechanical brake boosters.