Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Wei, Lingxiang; Feng, Tianliu; Zhao, Pengfei; Liao, Mingjun

doi:10.7250/bjrbe.2021-16.518

Cited by 3 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T3 is the time taken between maximum eyes opening and 20% pupil opening after closing the eyes. T4 is the time taken between maximum eyes opening and 80% pupil opening after closing the eyes [17].…”

Section: 1design Of Fatigue Driving Detection Algorithmmentioning

confidence: 99%

Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Dong

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

Driver's fatigue driving and mood are two important factors that cause traffic accidents, and with the widespread use of mobile devices, it is of significant research value to detect driver's abnormal driving behavior on the mobile terminal. In this paper, the PFLD model is used on mobile to obtain face keypoints and improve the EAR by fusing binocular opening and closing degree to detect abnormal driving behavior such as fatigue and mood. Fatigue detection integrates the improved EAR, yawning duration, head pitch angle and other multiple features to determine, which mainly includes the process of extracting the driver's both eyes and mouth features through the PFLD model, monitoring the driver's heart rate and head distance with Internet of Things devices, constructing feature vectors and using SVM to determine driving fatigue. The threshold parameters are selected by calculating the five-dimensional features of the face, and the driver's mood is detected based on KNN. Through experiments, the driver fatigue detection accuracy is about 90% and the mood recognition accuracy is up to 96%.

show abstract

Section: 1design Of Fatigue Driving Detection Algorithmmentioning

confidence: 99%

Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Dong

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

show abstract

“…Usually, mouth features are combined with eye features to ensure drivers' psychophysiological states. Mouth features include yawning frequency, mouth opening ratio, mouth state, mouth aspect ratio (MAR), and Open Mouth Rate (OMR) Liu et al 2020;Rathi et al, 2021;Valsan et al 2021;Wang and Qu 2021;Wei et al 2021;Zhao et al 2021;Zhongwei et al 2021;Zhuang and Qi 2021).…”

Section: Literature Review Of Drivers' Psychophysiological State Dete...mentioning

confidence: 99%

“…The Bayesian extension of the SVM is called Relevance Vector Machine (RVM) and it is used to classify facial features. Compared to SVM, RVM requires fewer training cases (Wei et al 2021).…”

Section: Analysis Of Drivers' Psychophysiological State Detection Mea...mentioning

confidence: 99%

Development of Drivers’ Psychophysiological State Detection Measures and Relevant Machine Learning Methods

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

Driver impairment is yet another human factor that is likely to cause dangerous driving behavior (aka distracted driving), leading to safety hazards on the roads. Assuring acceptable drivers' cognitive state can improve safe driving performance. Behavioral and self-report measures are used to assess drivers' cognitive states, but psychophysiological measures can add more considerable value in the real-world driving context. Hence, monitoring, detecting, and responding to drivers' psychophysiological state compose a safety countermeasure that can reduce the risk of distracted

show abstract

A Distracted Driving Discrimination Method Based on the Facial Feature Triangle and Bayesian Network

Feng

Wei

Wenjuan

et al. 2023

BJRBE

View full text Add to dashboard Cite

Distracted driving is one of the main causes of road crashes. Therefore, effective distinguishing of distracted driving behaviour and its category is the key to reducing the incidence of road crashes. To identify distracted driving behaviour accurately and effectively, this paper uses the head posture as a relevant variable and realizes the classification of distracted driving behaviour based on the relevant literature and investigation. A distracted driving discrimination algorithm based on the facial feature triangle is proposed. In the proposed algorithm, the Bayesian network is employed to judge driving behaviour categories. The proposed algorithm is verified by experiments using data from 20 volunteers. The experimental results show that the discrimination accuracy of the proposed algorithm is as high as 90%, which indicates that the head posture parameters used in this study are closely related to the distracted driving state. The results show that the proposed algorithm achieves high accuracy in the discrimination and classification of distracted driving behaviour and can effectively reduce the accident rate caused by distracted driving. Moreover, it can provide a basis for the research of distracted driving behaviour and is conducive to the formulation of the corresponding laws and regulations.

show abstract

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Cited by 3 publications

References 29 publications

Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Development of Drivers’ Psychophysiological State Detection Measures and Relevant Machine Learning Methods

A Distracted Driving Discrimination Method Based on the Facial Feature Triangle and Bayesian Network

Contact Info

Product

Resources

About