【摘要】：隨著社會的進步和人民生活水平的逐步提高,汽車保有量逐年增加,錯綜復(fù)雜的道路交通環(huán)境導(dǎo)致交通事故頻繁發(fā)生,疲勞駕駛已成為交通事故的主要原因。目前的疲勞駕駛檢測裝置通常采用接觸式生理信號檢測駕駛員的疲勞狀態(tài),這種方式不僅導(dǎo)致駕駛員感覺不舒適,而且干擾駕駛員正常駕駛。非接觸式圖像傳感器檢測駕駛員面部信息的疲勞駕駛檢測方法,雖克服了接觸式檢測的準確度低、可靠性差等缺點,但是容易受到外界環(huán)境的干擾。因此,研究多源信息融合的非接觸式疲勞駕駛檢測裝置可以有效減少交通事故。本文設(shè)計了基于雷達探測生理信號和方向盤角度檢測多源信息融合的檢測系統(tǒng),該系統(tǒng)主要包括多普勒雷達生理信號檢測系統(tǒng)、方向盤角度檢測系統(tǒng)、駕駛視頻信號記錄系統(tǒng)。首先根據(jù)多普勒雷達探測生理信號原理,設(shè)計硬件系統(tǒng),通過對雷達信號進行預(yù)處理、放大、有源帶通濾波、模數(shù)轉(zhuǎn)換等一系列的信號處理,從中獲取與生理信息相關(guān)的雷達數(shù)字信號。方向盤角度采集系統(tǒng)采用霍爾轉(zhuǎn)角傳感器和旋轉(zhuǎn)編碼器相結(jié)合的方法實現(xiàn)對方向盤角度信號的采集。其次針對雷達數(shù)字信號特點,采用FIR和IIR兩種不同數(shù)字濾波方法對生理信號進行分離,對比分離算法的優(yōu)缺點,采用零相位IIR濾波算法分離出呼吸信號和心跳信號。最后使用學生T檢驗法對疲勞駕駛視頻信號和同步的呼吸、心跳信號特征值進行統(tǒng)計分析,得到與疲勞程度相關(guān)聯(lián)的信號量,并設(shè)計極限學習機前饋神經(jīng)網(wǎng)絡(luò)算法識別疲勞程度。提取不同疲勞程度下生理信號和方向盤信號特征值,通過極限學習機算法訓練識別,并對疲勞樣本數(shù)據(jù)進行識別測試。學生T檢驗法統(tǒng)計駕駛員疲勞數(shù)據(jù),結(jié)論表明駕駛員的呼吸深度與疲勞駕駛具有較強的依賴性。實驗數(shù)據(jù)波形表明方向盤轉(zhuǎn)角變化、駕駛員呼吸幅度和呼吸頻率都隨睡眠加深趨向減小。選取駕駛員疲勞樣本進行識別測試,測試表明疲勞狀態(tài)下的識別率達到81%。多源信息融合的非接觸式疲勞駕駛檢測系統(tǒng)具有預(yù)測性和較高的識別準確度。
[Abstract]:With the progress of society and the gradual improvement of people's living standard, the number of cars is increasing year by year. The complicated road traffic environment leads to frequent traffic accidents. Fatigue driving has become the main cause of traffic accidents. The current fatigue driving detection device usually uses the contact physiological signal to detect the driver's fatigue state, which not only causes the driver to feel uncomfortable, but also interferes with the driver's normal driving. The fatigue driving detection method based on contactless image sensor to detect driver's face information overcomes the shortcomings of low accuracy and poor reliability of contact detection, but it is easily disturbed by external environment. Therefore, the study of multi-source information fusion non-contact fatigue driving detection device can effectively reduce traffic accidents. In this paper, a multi-source information fusion system based on radar detection physiological signal and steering wheel angle detection is designed. The system mainly includes Doppler radar physiological signal detection system, steering wheel angle detection system and driving video signal recording system. Firstly, according to the principle of Doppler radar detecting physiological signal, the hardware system is designed, and a series of signal processing, such as preprocessing, amplifying, active bandpass filtering, A / D conversion and so on, are carried out. The radar digital signal related to physiological information is obtained from it. The steering wheel angle acquisition system combines Hall angle sensor and rotary encoder to collect angle signal of steering wheel. Secondly, aiming at the characteristics of radar digital signal, two different digital filtering methods, FIR and IIR, are used to separate the physiological signal. The advantages and disadvantages of the separation algorithm are compared, and the breathing signal and the heartbeat signal are separated by the zero-phase IIR filtering algorithm. Finally, the student T-test method is used to analyze the characteristic values of fatigue driving video signal and synchronized breathing and heartbeat signal, and the signal quantity associated with fatigue degree is obtained. And designed the extreme learning machine feedforward neural network algorithm to identify the fatigue degree. The characteristic values of physiological signal and steering wheel signal were extracted under different fatigue degree, and the identification was trained by extreme learning machine algorithm, and the fatigue sample data were identified and tested. The student T test method was used to calculate the fatigue data of the driver. The conclusion showed that the breathing depth of the driver was strongly dependent on the fatigue driving. The experimental data waveforms show that the steering wheel angle changes and the breathing amplitude and frequency of the driver decrease with the deepening of sleep. Driver fatigue samples are selected for identification test. The results show that the identification rate of fatigue state reaches 81%. The multi-source information fusion-based non-contact fatigue driving detection system has the advantages of predictability and high recognition accuracy.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP274

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