|تعداد مشاهده مقاله||2,477,312|
|تعداد دریافت فایل اصل مقاله||1,746,037|
|Journal of Computational & Applied Research in Mechanical Engineering (JCARME)|
|مقاله 10، دوره 11، شماره 2 - شماره پیاپی 22، خرداد 2022، صفحه 409-423 اصل مقاله (1.59 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jcarme.2021.7412.1987|
|Ali Mirmohammad Sadeghi؛ Abdollah Amirkhani* ؛ Behrooz Mashadi|
|School of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran|
|تاریخ دریافت: 20 مهر 1399، تاریخ بازنگری: 17 تیر 1400، تاریخ پذیرش: 28 تیر 1400|
|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.|
|Vehicle safety systems؛ Clustering؛ K-means Algorithm؛ Hydraulic brake system|
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