發(fā)布時(shí)間：2018-07-31 12:31

【摘要】：近十年來(lái)我國(guó)高速交通網(wǎng)絡(luò)得到快速的發(fā)展,伴隨著人們出行便利的同時(shí),由交通事故帶來(lái)的安全隱患也變的越來(lái)越大。據(jù)美國(guó)國(guó)家高速公路安全專家對(duì)交通事故的調(diào)查分析,90%以上的事故都是駕駛員自身因素造成,而因車輛故障造成的交通事故僅占3%左右。通過(guò)對(duì)我國(guó)高速公路交通事故數(shù)據(jù)統(tǒng)計(jì),由車輛與車輛之間造成的事故占高速公路事故總量的50%以上,車輛追尾造成的事故約占車與車事故的35%,其中追尾碰撞的主要原因是由駕駛員疏忽造成的。特別是在高速公路上,駕駛員一點(diǎn)點(diǎn)小失誤可能就會(huì)給自己和他人帶來(lái)沉重的災(zāi)難。對(duì)駕駛員駕駛行為和駕駛狀態(tài)的判斷和分析以及汽車防追尾預(yù)警技術(shù)的運(yùn)用可以有效地降低因駕駛員行為造成的交通事故。本文對(duì)駕駛員狀態(tài)辨識(shí)和安全預(yù)警模型進(jìn)行了相關(guān)研究,具體內(nèi)容如下:第一,根據(jù)已有的研究成果對(duì)駕駛員狀態(tài)與駕駛員生理信號(hào)、駕駛行為及眼部信號(hào)之間的關(guān)系做了全面的歸納和分析,并確定了不同狀態(tài)下駕駛員心率信號(hào)、轉(zhuǎn)向盤信號(hào)及眼部特征的臨界值,為駕駛員狀態(tài)辨識(shí)提提供理論基礎(chǔ)。第二,人眼檢測(cè)算法的研究。駕駛員眼部特征的檢測(cè)直接關(guān)系到駕駛員狀態(tài)的判別,本文提出了一種混合膚色模型、積分投影及Canny邊緣檢測(cè)相結(jié)合的駕駛員眼睛快速定位的算法。首先根據(jù)人的膚色特征建立了混合膚色模型確定人臉位置,通過(guò)垂直積分投影和模板設(shè)計(jì)確定人臉區(qū)域,然后通過(guò)水平積分投影和投影曲線優(yōu)化確定的眼睛范圍,最后利用Canny算子對(duì)人眼區(qū)域進(jìn)行邊緣檢測(cè)和形態(tài)學(xué)處理進(jìn)行眼睛精確定位,通過(guò)仿真該算法的準(zhǔn)確率達(dá)到90%以上。第三,高速公路駕駛員狀態(tài)的辨識(shí)。高速公路單調(diào)的駕駛環(huán)境下駕駛員很容易疲勞和走神。通過(guò)模糊綜合評(píng)價(jià)和DS證據(jù)理論對(duì)駕駛員眼部特征、駕駛行為和心率變化信息進(jìn)行融合綜合判斷駕駛員狀態(tài),構(gòu)建了基于模糊評(píng)價(jià)-DS證據(jù)理論的駕駛員狀態(tài)的辨識(shí)模型并通過(guò)實(shí)例驗(yàn)證了其有效性。第四,建立了基于駕駛員狀態(tài)的安全車距模型。首先通過(guò)駕駛艙模擬試驗(yàn),獲取不同狀態(tài)下的駕駛員制動(dòng)反應(yīng)時(shí)間,并以這些試驗(yàn)數(shù)據(jù)為基礎(chǔ),根據(jù)不同狀態(tài)下的駕駛員確定相應(yīng)反應(yīng)時(shí)間,然后通過(guò)分析車輛減速過(guò)程和前車運(yùn)行狀態(tài),推導(dǎo)出車輛高速運(yùn)動(dòng)下安全車距模型并實(shí)時(shí)修正安全車距模型參數(shù)。最后,將修正后模型與典型安全車距模型進(jìn)行了仿真對(duì)比,通過(guò)對(duì)比得到修正模型的可靠性更高。
[Abstract]:With the rapid development of high-speed transportation network in China in the past ten years, with the convenience of travel, the hidden dangers caused by traffic accidents are becoming more and more serious. According to the investigation and analysis of traffic accidents by national highway safety experts, more than 90% of accidents are caused by drivers' own factors, but only about 3% of traffic accidents are caused by vehicle failures. According to the statistics of expressway traffic accidents in China, the accidents caused by vehicles and vehicles account for more than 50% of the total number of highway accidents. The accidents caused by vehicle rearward account for about 35% of vehicle accidents, among which the main cause of rear-end collision is caused by the driver's negligence. Especially on the freeway, a small error by a driver can cause a heavy disaster for himself and others. The judgment and analysis of driver's driving behavior and driving state and the application of anti-rear-end warning technology can effectively reduce the traffic accidents caused by driver's behavior. This paper has carried on the correlation research to the driver state identification and the safety early warning model, the concrete contents are as follows: first, according to the existing research results, the driver state and the driver physiological signal, The relationship between driving behavior and eye signals is summarized and analyzed, and the critical values of heart rate signal, steering wheel signal and eye characteristics are determined in different states, which provides a theoretical basis for driver state identification. Second, the research of human eye detection algorithm. The detection of driver's eye features is directly related to the identification of driver's state. In this paper, a hybrid skin color model, integral projection and Canny edge detection are proposed to locate the driver's eyes quickly. Firstly, a mixed skin color model is established to determine the position of human face, and the vertical integral projection and template design are used to determine the face area. Then, the eye range is optimized by horizontal integral projection and projection curve. Finally, the Canny operator is used to detect the edge of the human eye region and to locate the eye accurately by morphological processing. The accuracy of the algorithm is over 90% through simulation. Third, the identification of the state of the motorway driver. In the monotonous driving environment of the freeway, the driver is easily tired and distracted. Through fuzzy comprehensive evaluation and DS evidence theory, the driver's eye characteristics, driving behavior and heart rate change information are fused to judge the driver's state. The identification model of driver state based on fuzzy evaluation-DS evidence theory is constructed and its validity is verified by an example. Fourthly, a safe distance model based on driver state is established. First of all, through the cockpit simulation test, the driver braking reaction time in different states is obtained, and based on these test data, the corresponding reaction time is determined according to the driver under different conditions. Then by analyzing the vehicle deceleration process and the running state of the front car, the safe distance model under the high speed motion of the vehicle is derived and the parameters of the safety distance model are revised in real time. Finally, the modified model is compared with the typical safety vehicle distance model, and the reliability of the modified model is higher.
【學(xué)位授予單位】：山東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U463.6

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