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具有自动驾驶功能车辆的驾驶员困倦多方法检测

Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.

作者信息

Beles Horia, Vesselenyi Tiberiu, Rus Alexandru, Mitran Tudor, Scurt Florin Bogdan, Tolea Bogdan Adrian

机构信息

Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania.

出版信息

Sensors (Basel). 2024 Feb 28;24(5):1541. doi: 10.3390/s24051541.

DOI:10.3390/s24051541
PMID:38475079
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10934756/
Abstract

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.

摘要

本文概述了开发一种模糊决策算法的各种方法，该算法旨在监测驾驶员困倦情况并发出警告。该算法基于对眼电图（EOG）信号和眼睛状态图像的分析，以预防事故。困倦警告系统包括一些关键组件，这些组件了解、分析驾驶员的警觉状态并做出决策。如果驾驶员被识别为处于困倦状态，该分析结果可触发警告。驾驶员困倦的特征是对道路和交通的注意力逐渐下降、驾驶技能减弱以及反应时间增加，所有这些都会增加事故风险。在驾驶员未对警告做出反应的情况下，高级驾驶辅助系统（ADAS）应进行干预，接管车辆的指令控制。

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2
Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.基于深度神经网络的 ECG 和呼吸信号同步分析实现驾驶员瞌睡的多级分类
Int J Environ Res Public Health. 2022 Aug 29;19(17):10736. doi: 10.3390/ijerph191710736.
3
A Review of Recent Developments in Driver Drowsiness Detection Systems.
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Sensors (Basel). 2022 Mar 7;22(5):2069. doi: 10.3390/s22052069.
4
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems.脑电信号特征及其在驾驶员困倦检测系统中的应用综述。
Sensors (Basel). 2021 May 30;21(11):3786. doi: 10.3390/s21113786.
5
A Novel Classification Method for a Driver's Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures.基于心电图信号的心动间隔转移到图片上的一种驾驶员认知应激水平的新型分类方法。
Sensors (Basel). 2020 Feb 29;20(5):1340. doi: 10.3390/s20051340.
6
A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.利用生理信号的混合方法检测驾驶员困倦，以提高系统性能和可穿戴性。
Sensors (Basel). 2017 Aug 31;17(9):1991. doi: 10.3390/s17091991.
7
Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera.使用 EOG 和驾驶员摄像头检测条件自动化驾驶和手动驾驶中的不同驾驶员状态的眨眼。
Behav Res Methods. 2018 Jun;50(3):1088-1101. doi: 10.3758/s13428-017-0928-0.
8
Brain-computer interfaces in neurological rehabilitation.神经康复中的脑机接口
Lancet Neurol. 2008 Nov;7(11):1032-43. doi: 10.1016/S1474-4422(08)70223-0. Epub 2008 Oct 2.
9
Variational Bayesian least squares: an application to brain-machine interface data.变分贝叶斯最小二乘法：在脑机接口数据中的应用。
Neural Netw. 2008 Oct;21(8):1112-31. doi: 10.1016/j.neunet.2008.06.012. Epub 2008 Jun 27.
10
A fast learning algorithm for deep belief nets.一种用于深度信念网络的快速学习算法。
Neural Comput. 2006 Jul;18(7):1527-54. doi: 10.1162/neco.2006.18.7.1527.