【摘要】：駕駛員疲勞駕駛在道路交通事故發(fā)生的原因中占有絕大部分比例。鑒于視覺特征信息直觀明顯,易于檢測,并可實(shí)現(xiàn)非接觸性測量,因此基于視覺特征檢測駕駛員疲勞狀態(tài)已經(jīng)成為學(xué)者們研究的熱點(diǎn)和主流。以往研究大多采用一種或幾種駕駛員疲勞時的表現(xiàn)特征運(yùn)用貝葉斯網(wǎng)絡(luò)、模糊推理、人工神經(jīng)網(wǎng)絡(luò)、機(jī)器視覺等開展研究,其局限性在于忽略了駕駛員精神狀態(tài)的變化是一個隨時間變化的過程。本文所建立的隱馬爾科夫模型合理地反應(yīng)了駕駛員精神狀態(tài)變化與自身特征信息變化過程,它可以描述駕駛員疲勞狀態(tài)的在時間上的整體非平穩(wěn)性和局部平穩(wěn)性,是一種較為理想的駕駛員疲勞評估模型。本文選取了20位實(shí)驗(yàn)對象利用駕駛模擬器模擬高速公路工況。實(shí)驗(yàn)過程中運(yùn)用SMI-HED頭盔式眼動儀采集實(shí)驗(yàn)中駕駛員的眼部特征信息,攝像機(jī)記錄實(shí)驗(yàn)中駕駛員面部視頻圖像,利用生理參數(shù)測試儀采集駕駛員的生理信號。具體工作如下：1.在各種駕駛員疲勞評估模型中,本文重點(diǎn)分析了隱馬爾科夫模型(Hidden Markov Models,簡稱為HMM)的駕駛員疲勞評估模型。選取參數(shù)PERCLOS.AECS.PERLVO作為評估駕駛員疲勞狀態(tài)的參數(shù)變量,并建立對應(yīng)的HMM駕駛員疲勞評估模型。利用實(shí)驗(yàn)數(shù)據(jù)對所建立的模型進(jìn)行了訓(xùn)練,利用Baum.Welch算法(也稱前、后向算法)和基于樣本數(shù)據(jù)得到的HMM模型參數(shù)訓(xùn)練得到HMM駕駛員疲勞評估模型的最終參數(shù)λ=[A,B,π],使Pr[O/λ]達(dá)到最大。2.本文以生理參數(shù)儀獲得的數(shù)據(jù)為基礎(chǔ),通過與所建立的HMM駕駛員疲勞評估模型疲勞概率及HMM經(jīng)典算法Viterbi算法推斷駕駛員產(chǎn)生觀察值序列時間段內(nèi)最可能的精神狀態(tài)對比,驗(yàn)證了所建模型的合理性、準(zhǔn)確性。3.基于實(shí)驗(yàn)數(shù)據(jù),本文對所建立的駕駛員疲勞評估模型進(jìn)行了詳細(xì)的對比分析,并獲得了相應(yīng)的分析結(jié)論。結(jié)果表明,基于單參數(shù)和多參數(shù)所建立的HMM駕駛員疲勞評估模型能反應(yīng)駕駛員疲勞是一個隨時間變化的過程,這說明所建立的模型可以準(zhǔn)確反映駕駛員疲勞形成的時變特征。相應(yīng)模型的對比分析結(jié)果表明,基于多參數(shù)PERCLOS、 AECS、PERLVO的HMM的駕駛員疲勞評估模型更符合駕駛員真實(shí)的精神狀態(tài)變化過程,且根據(jù)觀察值序列得到的最可能隱藏的駕駛員精神狀態(tài)序列與駕駛員真實(shí)的精神狀態(tài)序列更加吻合。
[Abstract]:Driver fatigue driving accounts for the majority of the causes of road traffic accidents. Because the visual feature information is intuitionistic, easy to detect, and can realize non-contact measurement, the detection of driver fatigue state based on visual features has become the focus and mainstream of scholars. In the past, most of the previous studies used one or more kinds of performance characteristics of driver fatigue by using Bayesian network, fuzzy reasoning, artificial neural network, machine vision and so on. Its limitation is that it ignores that the change of driver's mental state is a process that changes with time. The hidden Markov model established in this paper reasonably reflects the change process of driver's mental state and its own characteristic information, and it can describe the overall nonstationarity and local stationarity of driver's fatigue state in time. It is an ideal driver fatigue evaluation model. In this paper, 20 experimental objects are selected to simulate the working conditions of expressway by driving simulator. In the course of the experiment, the SMI-HED head helmet eye movement instrument was used to collect the driver's eye characteristic information, the camera recorded the driver's facial video image, and the physiological parameter tester was used to collect the driver's physiological signal. The specific work is as follows: 1. Among all kinds of driver fatigue evaluation models, this paper focuses on the driver fatigue evaluation model of hidden Markov model (Hidden Markov Models, (HMM). The parameter PERCLOS.AECS.PERLVO is selected as the parameter variable to evaluate the driver's fatigue state, and the corresponding HMM driver fatigue evaluation model is established. The model is trained by experimental data, and the final parameter 位 = [A, B] of HMM driver fatigue evaluation model is obtained by using Baum.Welch algorithm (also known as forward and backward algorithm) and HMM model parameters training based on sample data. 蟺], so that Pr [O / 位] reaches the maximum. 2. Based on the data obtained by the physiological parameter instrument, this paper infers the most likely mental state within the time period of the observed value series by comparing the fatigue probability of the established HMM driver fatigue evaluation model with the HMM classical algorithm Viterbi algorithm. The rationality and accuracy of the model are verified. Based on the experimental data, the driver fatigue evaluation model is compared and analyzed in detail, and the corresponding analysis conclusions are obtained. The results show that the HMM driver fatigue evaluation model based on single parameter and multi-parameter can reflect that driver fatigue is a time-varying process, which indicates that the established model can accurately reflect the time-varying characteristics of driver fatigue formation. The comparative analysis results of the corresponding model show that the driver fatigue evaluation model based on multi-parameter PERCLOS, AECS,PERLVO HMM is more in line with the real mental state change process of the driver. The most likely hidden driver mental state sequence according to the observed value sequence is more consistent with the driver's real mental state sequence.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491.254

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 王榮本;顧柏園;郭烈;余天洪;;基于分形盒子維數(shù)的車輛定位和識別方法[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2006年03期

2 孫偉;張為公;張小瑞;呂成緒;陳剛;;疲勞駕駛檢測方法的研究進(jìn)展[J];汽車技術(shù);2009年02期

，

本文編號：2487669