Journal of Computational & Applied Research in Mechanical Engineering (JCARME)

Document Type : Research Paper

Authors

School of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran

https://doi.org/10.22061/jcarme.2021.7412.1987

Abstract

Recognizing a driver’s braking intensity plays a pivotal role in developing modern driver assistance and energy management systems. Therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a strategy 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. By doing so, the best number of clusters and positions of the centroids can be determined. 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.

Graphical Abstract

Keywords

Main Subjects

Automation

References

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