【摘要】：隨著車輛保有量的上升,駕駛安全日益引起人們的重視。駕駛員作為交通系統(tǒng)的重要組成部分,其駕駛狀態(tài)直接決定了整個(gè)交通系統(tǒng)的安全水平。而在眾多影響駕駛員狀態(tài)的因素中,駕駛疲勞最為常見,因此,如何對(duì)駕駛員的駕駛狀態(tài)進(jìn)行實(shí)時(shí)有效的檢測,并在疲勞發(fā)生時(shí)及時(shí)發(fā)出預(yù)警,對(duì)于提升交通安全水平,降低事故率具有重大意義。針對(duì)上述問題,本文在充分借鑒現(xiàn)有研究成果的基礎(chǔ)上,構(gòu)建了一種基于心電信號(hào)的駕駛疲勞識(shí)別模型。本文的主要研究內(nèi)容包括以下幾點(diǎn):1.首先介紹了駕駛疲勞檢測的背景及研究意義,分析國內(nèi)外相關(guān)研究在研究對(duì)象、研究方法和研究結(jié)論上的最新進(jìn)展,隨后提出本文的研究內(nèi)容和技術(shù)路線。2.從認(rèn)知心理學(xué)角度闡述了駕駛疲勞產(chǎn)生機(jī)理,分析了駕駛疲勞的誘發(fā)因素及疲勞表征。詳細(xì)介紹了心電信號(hào)的處理方法和指標(biāo)提取理論,通過對(duì)心率變異性和R-R間期的分析,確定了 R-R間期的提取方法,并結(jié)合數(shù)據(jù)對(duì)該方法進(jìn)行了驗(yàn)證,為進(jìn)一步提取可有效表征駕駛員生理狀態(tài)的心電指標(biāo)奠定了理論基礎(chǔ)。3.設(shè)計(jì)了雙任務(wù)范式的長時(shí)間模擬駕駛實(shí)驗(yàn),主任務(wù)為車輛跟馳,次任務(wù)是對(duì)剎車信號(hào)的按鍵反應(yīng)。通過分析實(shí)時(shí)采集的被試心電和行為數(shù)據(jù)后發(fā)現(xiàn),隨著疲勞的發(fā)生,被試心電指標(biāo)和行為指標(biāo)均呈現(xiàn)一定的趨勢(shì)性,且經(jīng)過顯著性分析,實(shí)驗(yàn)前期和后期,大部分心電指標(biāo)差異顯著,因此,可初步判斷出對(duì)駕駛疲勞指向性較好的心電指標(biāo)。4.闡述了將反應(yīng)時(shí)間作為疲勞等級(jí)劃分依據(jù)的可行性。通過分析整個(gè)實(shí)驗(yàn)過程中簡單反應(yīng)時(shí)間的變化規(guī)律,提出了利用反應(yīng)時(shí)間劃分疲勞等級(jí)。首先將實(shí)驗(yàn)過程劃分為若干時(shí)段,以第1時(shí)段的駕駛狀態(tài)為輕度疲勞狀態(tài),通過顯著性分析,對(duì)剩余時(shí)段的駕駛狀態(tài)進(jìn)行標(biāo)定,區(qū)分輕重兩種疲勞狀態(tài)。此外,通過與反應(yīng)時(shí)間的相關(guān)性分析,提取了可有效反映疲勞狀態(tài)的心電指標(biāo),構(gòu)成疲勞識(shí)別心電指標(biāo)集。5.利用SVM理論構(gòu)建了疲勞識(shí)別模型,通過不斷調(diào)整疲勞識(shí)別心電指標(biāo)集構(gòu)成和核函數(shù),對(duì)模型的識(shí)別效果進(jìn)行分析,發(fā)現(xiàn)綜合選用時(shí)域和頻域指標(biāo)以及RBF核函數(shù)時(shí),模型的識(shí)別效果最優(yōu)。最后利用實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證了上述模型的有效性。
[Abstract]:With the increase of vehicle ownership, people pay more and more attention to driving safety. As an important part of traffic system, driver's driving state directly determines the safety level of the whole traffic system. Driving fatigue is the most common among the many factors that affect driver's condition. Therefore, how to detect driver's driving state in real time and give out early warning in time when fatigue occurs can improve the level of traffic safety. It is of great significance to reduce the accident rate. In order to solve the above problems, a driving fatigue identification model based on ECG signal is constructed based on the existing research results. The main contents of this paper include the following: 1. This paper first introduces the background and significance of driving fatigue detection, analyzes the latest progress in the research object, research methods and research conclusions at home and abroad, and then puts forward the research content and technical route of this paper. 2. From the perspective of cognitive psychology, this paper expounds the mechanism of driving fatigue, and analyzes the inducing factors and fatigue characteristics of driving fatigue. The processing method and index extraction theory of ECG signal are introduced in detail. Through the analysis of heart rate variability and R-R interval, the extraction method of R-R interval is determined, and the method is verified with data. It lays a theoretical foundation for the further extraction of ECG indexes which can effectively represent the physiological state of drivers. 3. A long time simulation driving experiment with dual task paradigm is designed. The main task is to follow the vehicle and the second task is to respond to the brake signal by keystroke. After analyzing the ECG and behavior data collected in real time, it was found that with the occurrence of fatigue, the ECG and behavioral indexes of the subjects showed a certain trend, and after significant analysis, the early and late stages of the experiment. There are significant differences in the majority of ECG indexes, so we can preliminarily judge the ECG index which has good directivity to driving fatigue. 4. 4. The feasibility of using reaction time as the basis of fatigue grade classification is expounded. By analyzing the variation rule of simple reaction time in the whole experiment process, it is put forward that the fatigue grade is divided by reaction time. Firstly, the experiment process is divided into several periods, and the driving state of the first period is regarded as mild fatigue state. Through the significant analysis, the driving state of the remaining period is calibrated to distinguish the heavy and heavy fatigue states. In addition, through the correlation analysis with the reaction time, the ECG indexes which can effectively reflect the fatigue state are extracted, and the set of ECG indexes for fatigue identification is constructed. The fatigue identification model is constructed by using SVM theory. By continuously adjusting the composition of ECG index set and kernel function of fatigue identification, the recognition effect of the model is analyzed. It is found that when the time domain, frequency domain index and RBF kernel function are synthetically selected, The recognition effect of the model is optimal. Finally, the validity of the model is verified by experimental data.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.25

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