An Adaptive System for Detecting Driving Abnormality of Individual Drivers Using Gaussian Mixture Model

Shakib, Md. Nazmus; Shamim, Md.; Shawon, Md. Nazirul Hasan; Isha, Most. Kaniz Fatema; Hashem, M. M. A.; Kamal, Abdus Samad

doi:10.1109/iceeict53905.2021.9667850

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…In contrast to the abovementioned clustering algorithms, it employs soft clustering, a data point may be contained in different clusters with different probabilities. Shakib et al [77] holds that sudden changes in driving style can damage system stability, as each data point can only be associated with one driving style in the case of hard clustering, which leads to difficulty in monitoring abnormal driving behavior, i.e, short-term abnormal driving style. Thus, he used a GMM-based clustering algorithm to recognize short-term driving styles.…”

Section: • Gmmmentioning

confidence: 99%

A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives

Chu,

Zhuang,

Wang

et al. 2023

IEEE Trans. Intell. Veh.

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Driving style recognition provides an effective way to understand human driving behaviors and thereby plays an important role in the automotive sector. However, most works fail to consider the influence of deploying the recognition results on the vehicle side, which requires real-time recognition performance. To facilitate the application of driving styles in automotive, we survey related advances in driving style recognition along short-and long-term pipelines. We first defined short-and long-term driving styles and then described the input data used by the recognition models and related data-processing techniques. Furthermore, we also revisited existing evaluation metrics for different recognition algorithms. Finally, we discussed the potential applications of driving style recognition in intelligent vehicles.

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Section: • Gmmmentioning

confidence: 99%

A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives

Chu,

Zhuang,

Wang

et al. 2023

IEEE Trans. Intell. Veh.

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show abstract

“…of RPM, and the average speed). Shakib et al [17] employed the maximal information coefficient (MIC) to choose features while extending to the complex input. Multiple models, including GMM, Fuzzy C-means, and K-means, are used to assess the complex input features, with compelling results in each case.…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

“…The versatility of the collected vehicle data (e.g., controller area network (CAN) bus, image, gyroscope) is prone to indigestion in the mathematical-based traditional models. Multi-dimensional time-series data are challenging to apply to the majority of statistical models, including hidden Markov model (HMM) [9][10][11][12][13][14], Gaussian mixture model (GMM) [15][16][17][18][19], support vector machine (SVM) [20][21][22][23][24][25][26][27], Naive Bayes (NB) [28][29][30], fuzzy logic (FL) [31][32][33][34][35][36], and k-nearest neighbor (KNN) [20,[37][38][39][40]. The deep learning based models, including convolutional neural network (CNN) [41][42][43][44][45][46][47][48], recurrent neural network (RNN) [49][50][51][52][53]…”

Section: Introductionmentioning

confidence: 99%

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of CO2 being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

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“…Grounded in information theory principles, these criteria strike a balance between model fit and model complexity, facilitating the determination of the most suitable clustering solution. This solution is also proposed in [29] where the appropriate GMM number of clusters was decided to identify the driving style of car drivers. To further analyze the best scenario, the AIC [27] and BIC [28] scores were implemented.…”

Section: Gmm Clusteringmentioning

confidence: 99%

Low-Cost Optimized U-Net Model with GMM Automatic Labeling Used in Forest Semantic Segmentation

Andrei,

Grigore

2023

Sensors

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Currently, Convolutional Neural Networks (CNN) are widely used for processing and analyzing image or video data, and an essential part of state-of-the-art studies rely on training different CNN architectures. They have broad applications, such as image classification, semantic segmentation, or face recognition. Regardless of the application, one of the important factors influencing network performance is the use of a reliable, well-labeled dataset in the training stage. Most of the time, especially if we talk about semantic classification, labeling is time and resource-consuming and must be done manually by a human operator. This article proposes an automatic label generation method based on the Gaussian mixture model (GMM) unsupervised clustering technique. The other main contribution of this paper is the optimization of the hyperparameters of the traditional U-Net model to achieve a balance between high performance and the least complex structure for implementing a low-cost system. The results showed that the proposed method decreased the resources needed, computation time, and model complexity while maintaining accuracy. Our methods have been tested in a deforestation monitoring application by successfully identifying forests in aerial imagery.

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An Adaptive System for Detecting Driving Abnormality of Individual Drivers Using Gaussian Mixture Model

Cited by 6 publications

References 10 publications

A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives

A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Low-Cost Optimized U-Net Model with GMM Automatic Labeling Used in Forest Semantic Segmentation

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