Braking intensity recognition with optimal K-means clustering algorithm
Publish place: Journal of Computational and Applied Research in Mechanial Engineering، Vol: 11، Issue: 2
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JCARME-11-2_010
تاریخ نمایه سازی: 17 اسفند 1400
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.
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Authors
Ali Mirmohammad Sadeghi
School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran
Abdollah Amirkhani
School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran
Behrooz Mashadi
School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran
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