Detecting Driver Fatigue by Steering Wheel Grip Force

Lee, Kyehoon; Hyun, Sung-Ae; Oah, Shezeen

doi:10.5392/ijoc.2016.12.1.044

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Therefore, the detection methods of fatigue driving based on the vehicle driving parameters also receive a lot of attention in research field. The popular methods include steering wheel angle detection method, steering wheel grip detection method, vehicle speed detection method, vehicle offset detection method, brake pedal force detection method and the accelerator pedal force detection method [33][34][35].…”

Section: Vehicle Driving Parameters Based Detection Methodsmentioning

confidence: 99%

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A Review on Fatigue Driving Detection

Shi

Wang

2017

ITM Web Conf.

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Abstract:The socialization of automobile development has brought great convenience to people's travel. However, the rapid increase in the number of vehicles has also caused a series of problems. The increase in traffic accidents has brought great social casualties and economic losses. Fatigue driving, which is an important factor in the traffic accident, has aroused people's attention. This paper reviews all kinds of fatigue driving detection methods at present; compares various fatigue driving detection methods in terms of accuracy, real-time and cost; analyses the advantages and disadvantages of various methods; introduces the application of fatigue detection system in automobile; summarizes the current deficiencies and future development trends in the field of fatigue driving detection. The future research of this field will be more to the data fusion, computer vision and deep learning.

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Section: Vehicle Driving Parameters Based Detection Methodsmentioning

confidence: 99%

“…Based on this characteristic, researchers proposed an algorithm to analyze the change rule of the driving steering wheel grip [35]. The data was collected by the force sensor installed on the steering wheel.…”

Section: Steering Wheel Grip Detectionmentioning

confidence: 99%

A Review on Fatigue Driving Detection

Shi

Wang

2017

ITM Web Conf.

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“…Compared with the aforementioned methods, the monitoring method based on the grip force signal has several advantages, including simple operation and application, high signal-to-noise ratio, low cost, a monitoring process that does not affect the normal driving operation, and a possibility to be applied to the field of automatic driving . LEE developed a fatigue detection system by monitoring the grip force, and changes in the grip force were measured when participants were performing an interactive simulated driving task . Li developed a method that measures driver’s fatigue by detecting a driver’s grip force on the steering wheel while driving.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System

Lv,

Wang,

et al. 2024

ACS Appl. Mater. Interfaces

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Driver assistance systems can help drivers achieve better control of their vehicles while driving and reduce driver fatigue and errors. However, the current driver assistance devices have a complex structure and severely violate the privacy of drivers, hindering the development of driver assistance technology. To address these limitations, this article proposes an intelligent driver assistance monitoring system (IDAMS), which combines a Kresling origami structure-based triboelectric sensor (KOS-TS) and a convolutional neural network (CNN)-based data analysis. For different driving behaviors, the output signals of the KOS-TSs contain various features, such as a driver’s pressing force, pressing time, and sensor triggering sequence. This study develops a multiscale CNN that employs different pooling methods to process KOS-TS data and analyze temporal information. The proposed IDAMS is verified by driver identification experiments, and the results show that the accuracy of the IDAMS in discriminating eight different users is improved from 96.25% to 99.38%. In addition, the results indicate that IDAMS can successfully monitor driving behaviors and can accurately distinguish between different driving behaviors. Finally, the proposed IDAMS has excellent hands-off detection (HOD), identification, and driving behavior monitoring capabilities and shows broad potential for application in the fields of safety warning, personalization, and human–computer interaction.

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“…Detecting fatigue during driving presents a complex challenge, distinct from established methods for testing intoxicated driving through blood alcohol concentration [4,5]. In the initial stages of detecting driver fatigue, researchers commonly rely on the dynamic interaction between drivers and vehicles [6,7], encompassing factors like steering wheel grasp intensity, steering frequency, and steering wheel angle [8]. However, these behavioral indicators exhibit limited correlation with physiological conditions and can be notably influenced by the specific vehicle model.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

Li,

Liu,

Gao

et al. 2024

Sensors

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Background: Driving fatigue is a significant concern in contemporary society, contributing to a considerable number of traffic accidents annually. This study explores novel methods for fatigue detection, aiming to enhance driving safety. Methods: This study utilizes electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to monitor driver fatigue during simulated driving experiments lasting up to 7 h. Results: Analysis reveals a significant correlation between behavioral data and hemodynamic changes in the prefrontal lobe, particularly around the 4 h mark, indicating a critical period for driver performance decline. Despite a small participant cohort, the study’s outcomes align closely with established fatigue standards for drivers. Conclusions: By integrating fNIRS into non-voluntary attention brain function experiments, this research demonstrates promising efficacy in accurately detecting driving fatigue. These findings offer insights into fatigue dynamics and have implications for shaping effective safety measures and policies in various industrial settings.

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Detecting Driver Fatigue by Steering Wheel Grip Force

Abstract: Driver fatigue is a major cause of fatal road accidents and has significant implications in

Cited by 8 publications

References 18 publications

A Review on Fatigue Driving Detection

A Review on Fatigue Driving Detection

Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

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