A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data

Li, Zuojin; Yang, Qing; Chen, Shengfu; Zhou, Wei; Chen, Liukui; Song, Lei

doi:10.1177/1550147719872452

Cited by 28 publications

(8 citation statements)

References 22 publications

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“…In general, these solutions can be grouped into three categories according to the fatigue detection methods, which are based on monitoring (1) vehicle driving parameters, (2) driver physiological parameters, or (3) driver facial features [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The first category includes sensors for monitoring steering wheel touch, the steering wheel angle, the travel path and the vehicle’s speed [ 12 , 13 , 14 ]. Failure to detect a hand on the steering wheel or the detection of steering wheel turns beyond the lane may indicate unusual driver behaviour due to fatigue.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Dziuda

Baran

Zieliński

et al. 2021

Sensors

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This paper presents a camera-based prototype sensor for detecting fatigue and drowsiness in drivers, which are common causes of road accidents. The evaluation of the detector operation involved eight professional truck drivers, who drove the truck simulator twice—i.e., when they were rested and drowsy. The Fatigue Symptoms Scales (FSS) questionnaire was used to assess subjectively perceived levels of fatigue, whereas the percentage of eye closure time (PERCLOS), eye closure duration (ECD), and frequency of eye closure (FEC) were selected as eye closure-associated fatigue indicators, determined from the images of drivers’ faces captured by the sensor. Three alternative models for subjective fatigue were used to analyse the relationship between the raw score of the FSS questionnaire, and the eye closure-associated indicators were estimated. The results revealed that, in relation to the subjective assessment of fatigue, PERCLOS is a significant predictor of the changes observed in individual subjects during the performance of tasks, while ECD reflects the individual differences in subjective fatigue occurred both between drivers and in individual drivers between the ‘rested’ and ‘drowsy’ experimental conditions well. No relationship between the FEC index and the FSS state scale was found.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Dziuda

Baran

Zieliński

et al. 2021

Sensors

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“…Varying vertical localities allow filters of different widths L=3, 4, and 5 through the usage of filters with a width set by the size of the word embed vector. This makes it useful for learning many features [45].…”

Section: -2 Techniques and Methods Used In The Software Requirements ...mentioning

confidence: 99%

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

Ali¹,

Saleem²

2022

JES

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Both the functionality and the non-functionality for what the software system does and does not do within software systems requirements are documented in a Software Requirements Specification (SRS). In requirements engineering, system requirements classify into several categories such as functional, quality and constraint classes. Therefore, we evaluate several machine learning approaches as well as methodologies mentioned in previous literature in terms of automatic requirements extraction, then classification is performed based on methodically reviewing many previous works on software requirements classification to assist software engineers in selecting the best requirement classification technique. The study aims to obtain answers for several questions: "What were machine learning algorithms used for the classification process of the requirements?", "How do these algorithms work and how are they evaluated?", "What methods were used for extracting features from a text?", "What evaluation criteria were used in comparing results?", and "Which machine learning techniques and methods provided the highest accuracy?".

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“…Then vehicle-based methods integrate a system for detecting driver weariness using devices and sensors into the wheels of the vehicle. This embedded device track measures such as Steering Wheel Velocity (SWV), Steering Wheel Angle (SWA), Steering Wheel Movements (SWM), hand location, hand absences, and lane departure to identify driver actions (Li et al 2019). Driving events may quickly turn into hazardous scenarios due to fatigue.…”

Section: Introductionmentioning

confidence: 99%

Data-driven approach for effective fatigue detection in drivers during driving events

Vyas,

Tyagi,

Sharma

2023

Multidiscip. Sci. J.

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The effectiveness of transportation and the safety of the road depend heavily on drivers during driving events. The way people drive, and the likelihood of accidents or other occurrences may both be strongly impacted by their actions and behaviors. Promoting safe and sensible driving behaviors requires an understanding of the traits and variables that affect drivers during driving incidents. The rising number of traffic accidents highlights the critical necessity to rein in and lessen the prevalence of careless driving. One of the most common causes of these serious mistakes is drowsiness while driving. To combat this problem, algorithms have been created to identify signs of driver weariness and sound an alarm. The developed algorithms have a serious flaw in their accuracy, and it also takes too long to identify driver drowsiness before alerting them. Timeliness and precision are two of the most important factors in preventing traffic mishaps. Multiple datasets have been utilized to improve methods for identifying signs of exhaustion or drowsiness. These data were acquired either through video streaming records of the driver's behavior or from the driver's brain electroencephalogram (EEG) readings. In order to create a high-performance fatigue detection system, this research designs a novel firefly-integrated optimum cascaded convolutional neural network (FI-OCCNN). The suggested approach offers the greatest detection accuracy among the existing classifiers, up to 98.75%. The studies further show that the recommended methods provide the greatest level of detecting precision with the quickest testing time (TT) compared to all other existing and successful tiredness detection techniques.

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A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data

Cited by 28 publications

References 22 publications

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Classification of Software Systems attributes based on quality factors using linguistic knowledge and machine learning: A review.

Data-driven approach for effective fatigue detection in drivers during driving events

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