Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Han, Wei; Wang, Wenshuo; Li, Xiaohan; Jiang, Xi

doi:10.1049/iet-its.2017.0379

Cited by 53 publications

(33 citation statements)

References 34 publications

(42 reference statements)

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“…Choosing effective signals is crucial since any further actions and subsequent results would largely depend on it [25]. At present, characteristic parameters commonly employed to highlight the driving styles include the vehicle speed, acceleration, opening degree of the accelerator pedal, and jerk [16], [26]. Since the collected feature quantities, such as the vehicle speed and acceleration, are a series of continuous data, the first step is to translate them into a number of feature variables.…”

Section: Introductionmentioning

confidence: 99%

Research on Classification and Recognition of Driving Styles Based on Feature Engineering

et al. 2019

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Accurate classification and the effective recognition of driving styles are critical for improving control performance of the vehicle powertrain. In this research, a set of driving style classification and recognition methods is built based on the feature engineering. First, a specified road test is conducted considering the influence factors, and meanwhile, the corresponding driving data is collected, followed by a detailed evaluation of the driving styles. Then, the information entropy is applied to discretize the driving data, including the speed, acceleration, and opening degree of the accelerator pedal, and 44 feature quantities are extracted to characterize the driving style. By analyzing strong correlation and redundancy among the constructed feature quantities, the principal component analysis (PCA) is employed to reduce the dimension, and the fuzzy c-means (FCM) clustering algorithm is leveraged to classify the driving style. The successful classification rate reaches 92.16%, which is improved by 9.81% in comparison with traditional features. Finally, a parameter identification algorithm based on the support vector machine (SVM) is applied to identify the classified driving style, and the recognition accuracy reaches 92.86%, which is improved by 7.15% in comparison with traditional features, proving the feasibility of the proposed algorithm. INDEX TERMS Driving style classification, feature discretization, fuzzy c-means (FCM) clustering, support vector machine (SVM).

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Section: Introductionmentioning

confidence: 99%

Research on Classification and Recognition of Driving Styles Based on Feature Engineering

et al. 2019

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“…In the case of imbalanced training materials, problems such as gender discrimination and racial discrimination will occur, which is prone to bias. Bayesian probability control [23,24] Bayesian probabilistic control should measure the confidence of an individual for an uncertain proposition and use this property to control it, so it is subjective in this sense. Using the probability theory proposed by Bayes, we can examine the sensitivity of decision-making.…”

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Chang¹,

Chu²,

Lin³

et al. 2021

Automation and Control

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Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people's way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice.

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“…Note that blinks less than 0.5s are not taken into account. Learning techniques have also been used to classify different levels of vigilance from the EOG such as data partitioning analysis, the use of which has been suggested [38] or support vector machines. Authors of [39] Proposed using a model of Gaussian mixtures on Lissajous curves obtained from the horizontal and vertical channels of the EOG as well as movements of the head.…”

Section: Driver Drowsiness Detection Systems By Blinking Analysismentioning

confidence: 99%

Analysis of Driver Drowsiness Detection using EEG and EOG

Firke¹,

Jain²

2018

IJET

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This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.

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Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Cited by 53 publications

References 34 publications

Research on Classification and Recognition of Driving Styles Based on Feature Engineering

Research on Classification and Recognition of Driving Styles Based on Feature Engineering

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Analysis of Driver Drowsiness Detection using EEG and EOG

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