Automation Detection of Driver Fatigue Using Visual Behavior Variables

Wang, Yonggang; Ma, Jingfeng

doi:10.2478/ace-2018-0023

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Gaze points are normally the raw outputs from an eye-tracking device. Many commercialized eye trackers can record the gaze points, such as SMI iView [30], Tobii X 120 [31], Eye Tribe [32], Smart Eye [33], Mobileye [34], and Face lab [35] [36]. A single gaze point constitutes one raw sample of data captured by the eye tracking device.…”

Section: B Gaze Movements and Mental Statesmentioning

confidence: 99%

Eye Tracking Analytics for Mental States Assessment – A Review

Feng

2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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With the increasing demands on mutual understanding between human beings and advanced systems, objectively measuring and monitoring human mental states in a non-intrusive way has been a hot topic in recent days. Eye-tracking data has long been found to be a kind of suitable bio signals in measuring human mental states, as visual is the first channel of information collection and eye-tracking data shows the process of human-system interactions. Traditionally, many studies have been conducted to confirm the correlations between eye-tracking data and human mental states. Recently, with advanced artificial intelligence algorithms, the spatial and temporal patterns of eye-tracking data can detect human mental states. This study aims to explore and review eye-tracking parameters and state-of-art methods for mental states assessments. The study reveals that most of the studies focused on using statistical methods to process eye-tracking data. While novel methods, such as machine learning and deep learning are important aspects in strengthening eye-tracking analytics owing to their ability in dealing with dynamic and noisy data. Besides, novel features extracted from eye-tracking data, such as gaze-bin and entropy may greatly improve the performance in assessing human mental states.

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Section: B Gaze Movements and Mental Statesmentioning

confidence: 99%

Eye Tracking Analytics for Mental States Assessment – A Review

Feng

2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…However, vehicle parameters need to be measured during actual operation, which increases the cost of the vehicle. (3) Recognition based on the physiological parameters of the driver: the driver's fatigue state can be judged by identifying the driver's physiological characteristics, such as with electrocardiograms [ 4 ], electroencephalograms [ 5 , 6 ], electrooculograms [ 7 ], and electromyography [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Liu

Gao

et al. 2021

Journal of Healthcare Engineering

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Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

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“…However, there are few studies on the detection of fatigue degrees of freight vehicle drivers. Wang, Y. et al [12] carried out 2 h, 3 h, and 4 h natural driving tests of commercial drivers to test the correlation between drivers' visual behaviors and subjective fatigue degree.…”

Section: Introductionmentioning

confidence: 99%

Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

Wang

Ding

et al. 2021

Applied Sciences

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Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

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Automation Detection of Driver Fatigue Using Visual Behavior Variables

Cited by 4 publications

References 14 publications

Eye Tracking Analytics for Mental States Assessment – A Review

Eye Tracking Analytics for Mental States Assessment – A Review

A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

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