Data-Driven Identification of Characteristic Real-Driving Cycles Based on k-Means Clustering and Mixed-Integer Optimization

Förster, Daniel; Inderka, R.B.; Gauterin, Frank

doi:10.1109/tvt.2019.2963272

Cited by 41 publications

(8 citation statements)

References 25 publications

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“…Forster et al [152] study how to identify multiple characteristic driving cycles from vehicle data representing real driving scenarios. They use a k-means algorithm to find k groups of micro trips to represent different driving cycle features.…”

Section: Clustering With Unsupervised Learningmentioning

confidence: 99%

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Danba

Bao

Han³

et al. 2022

Sensors

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Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.

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Section: Clustering With Unsupervised Learningmentioning

confidence: 99%

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Danba

Bao

Han³

et al. 2022

Sensors

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“…Data mining techniques use supervised learning utilizes labeled data for training purposes [13], [14], [15], [16], [17]. Table 1 shows work related to driver identification and profiling using machine learning algorithms, such as Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) [18], K-Nearest Neighbor (KNN) [19], Multilayer Perceptrons (MLP), Fuzzy Neural Networks (FNN) [20] and K-means clustering [21]. Zhang et al [22] use HMM to analyze unique driving patterns using an artificial simulator.…”

Section: Related Workmentioning

confidence: 99%

ATVSA: Vehicle Driver Profiling for Situational Awareness

Khan

Saxena

Rana

et al. 2022

2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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Increasing connectivity and automation in vehicles leads to a greater potential attack surface. Such vulnerabilities within vehicles can also be used for auto-theft, increasing the potential for attackers to disable anti-theft mechanisms implemented by vehicle manufacturers. We utilize patterns derived from Controller Area Network (CAN) bus traffic to verify driver "behavior", as a basis to prevent vehicle theft. Our proposed model uses semi-supervised learning that continuously profiles a driver, using features extracted from CAN bus traffic. We have selected 15 key features and obtained an accuracy of 99% using a dataset comprising a total of 51 features across 10 different drivers. We use a number of data analysis algorithms, such as J48, Random Forest, JRip and clustering, using 94K records. Our results show that J48 is the best performing algorithm in terms of training and testing (1.95 seconds and 0.44 seconds recorded, respectively). We also analyze the effect of using a sliding window on algorithm performance, altering the size of the window to identify the impact on prediction accuracy.

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“…MC have a long popularity in generating synthetic driving cycles [10][11][12][13][14][15][16], but in recent years have been also successfully applied in predictive control applications [17][18][19][20][21][22][23][24][25][26][27]. Some works also compare MC with artificial neural networks (ANNs) [28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

“…As introduced in Section 3.2 discrete Markov processes states have to be defined. In accordance with [13,15] each state is represented by a 3 × 1 tuple consisting of vehicle velocity v i , acceleration a i , and road slope sl p i :…”

Section: Model Architecturesmentioning

confidence: 99%

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A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence

2022

Self Cite

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In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended.

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Data-Driven Identification of Characteristic Real-Driving Cycles Based on k-Means Clustering and Mixed-Integer Optimization

Cited by 41 publications

References 25 publications

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

ATVSA: Vehicle Driver Profiling for Situational Awareness

A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence

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