本文關(guān)鍵詞：面向駕駛員疲勞車道偏離識別方法研究　出處：《吉林大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 駕駛疲勞 疲勞車道偏離 車道偏離預(yù)警 ROC 預(yù)警系統(tǒng)

【摘要】：對近幾年道路交通事故統(tǒng)計(jì)資料的分析發(fā)現(xiàn),疲勞駕駛是導(dǎo)致重特大交通事故的主要因素之一.因此,運(yùn)用各種檢測手段對駕駛員的疲勞狀態(tài)進(jìn)行檢測已成為近幾年的研究熱點(diǎn).在交通事故發(fā)生前進(jìn)行報(bào)警并采取相應(yīng)措施,對于降低交通事故的發(fā)生率,減少人員傷亡及財(cái)產(chǎn)損失具有重要的社會意義和經(jīng)濟(jì)意義.本文提出了考慮駕駛員操縱特性與車輛運(yùn)動狀態(tài)特性的疲勞車道偏離識別方法,并做出駕駛員是否疲勞的判斷,在滿足科研團(tuán)隊(duì)國際合作項(xiàng)目需求的基上,為車道偏離預(yù)警系統(tǒng)決策提供一定的理論支持.主要工作如下:本文基于吉林大學(xué)汽車仿真與控制國家重點(diǎn)實(shí)驗(yàn)室的駕駛員在環(huán)試驗(yàn)臺設(shè)計(jì)了疲勞車道偏離試驗(yàn).試驗(yàn)以12名駕駛員為研究對象,采集了能夠反映駕駛員疲勞車道偏離的操縱特性與車輛運(yùn)動狀態(tài)特性的相關(guān)參數(shù).然后借助受試者工作特性曲線(ROC)確定能夠明顯區(qū)別駕駛員正常換道和疲勞車道偏離的識別時(shí)間窗.最終確定駕駛員車道偏離識別時(shí)間窗為3s.以此篩選有效樣本,并將樣本分為訓(xùn)練樣本和識別樣本.運(yùn)用獨(dú)立樣本T檢驗(yàn),分別量化了疲勞偏離和正常換道狀態(tài)對駕駛行為特性參數(shù)的影響,并提出了能夠分別疲勞車道偏離和正常換道的特性參數(shù).以駕駛行為特征參數(shù)作為觀測層,建立高斯-隱馬爾科夫疲勞車道偏離識別模型.為了對比不同特征參數(shù)類型對識別效果的影響,模型建立過程中采用了10類特征參數(shù)集分別對模型進(jìn)行了離線訓(xùn)練,得到了相應(yīng)的模型參數(shù).為了對比不同建模方法對識別效果的影響,我們進(jìn)行下面兩個(gè)步驟:步一:我們建立了兩類駕駛疲勞識別模型是基于支持向量機(jī)(SVM)的疲勞車道偏離識別模型和基于方向盤轉(zhuǎn)角速度時(shí)序分析的駕駛疲勞識別模型.首先,針對上面10類特征參數(shù)集,基于SVM理論建立了疲勞車道偏離識別模型,用于分別疲勞車道偏離及正常換道的狀態(tài).其次,利用上面已確定的識別時(shí)間窗,選取時(shí)間窗內(nèi)的方向盤轉(zhuǎn)角速度數(shù)據(jù)序列作為識別特征.當(dāng)識別特征滿足波動幅度約束與波動變化約束時(shí),則認(rèn)定該操作時(shí)段駕駛員存在疲勞.步二:通過模型識別效果分析,研究了特征參數(shù)類型及不同建模方法對識別效果的影響.結(jié)果表明:從準(zhǔn)確率、靈敏度和特異性三個(gè)模型評價(jià)函數(shù)綜合分析得出,與所建立的模型相比,基于GM-HMM建立的疲勞車道偏離識別模型識別效果最優(yōu).說明,研究成果能夠?yàn)槠囍鲃影踩o助系統(tǒng)的研究和應(yīng)用提供一定的理論和技術(shù)支持.
[Abstract]:The analysis of statistical data in recent years, the road traffic accident, driver fatigue is one of the main causes of serious traffic accidents. Therefore, using various means of detection of driver fatigue detection has become a hot research topic in recent years. When the traffic accident happened before the alarm and take corresponding measures to reduce traffic accidents. The incidence rate has important social and economic significance to reduce casualties and property losses. In this paper, considering the fatigue characteristics and driving lane vehicle motion deviation recognition method, and make the judgment whether the driver fatigue, to meet the needs of the project research team of international cooperation based on departure warning system to provide a decision the theoretical support for the lane. The main work is as follows: the Jilin University State Key Laboratory of automotive simulation and control based on driving The driver in the loop test platform is designed to test fatigue test. Lane departure 12 drivers as the research object, collected can reflect the related parameters of driver fatigue characteristics and operating characteristics of lane vehicle motion deviation. Then by means of the receiver operating characteristic curve (ROC) is able to identify significant differences for the driver's normal time window road and lane departure. Ultimately determine the fatigue of driver identification lane departure time window for 3s. to screen the effective sample, and the sample is divided into training samples and identifying samples. Using independent sample T test, respectively to quantify the fatigue and deviate from the normal state change impact on driving behavior characteristic parameters, and puts forward the characteristic parameters to lane departure and normal fatigue respectively. The lane changing driving behavior characteristic parameters as the observation layer, a Gauss Markov model. In order to identify the fatigue of lane departure Effects of different types of feature parameters than the recognition results, the process of modeling, using 10 kinds of characteristic parameter sets were carried out off-line training of the model, the corresponding parameters are obtained. In order to compare different modeling methods on the identification results, we carried out the following two steps: step one: we set up two kinds of driving fatigue recognition model is based on support vector machine (SVM) fatigue recognition model of lane departure and driving fatigue recognition model analysis of steering wheel angle velocity based on time series. Firstly, according to the above 10 kinds of feature parameters set, SVM theory established the fatigue recognition model based on lane departure, respectively for lane departure and normal fatigue state change. Secondly, using the recognition time window above have been identified, selected within the time window of the steering wheel angular velocity data sequence as the recognition feature. When the identification feature satisfies wave amplitude constraint Constraint and fluctuation, is that the operation time. Step two: driver fatigue identification effect through the model analysis, studied the effect of parameters and different types of modeling methods on the identification results. The results show that the accuracy, sensitivity and specificity of the three models on comprehensive analysis of price function that, compared with the established the optimal GM-HMM model, established from fatigue lane recognition model based on recognition results. That research results can provide some theoretical and technical support for the research and application of vehicle active safety auxiliary system.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：U463.6

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