【摘要】：隨著我國機動車數(shù)量的持續(xù)增長,道路交通安全問題也日益嚴峻,道路交通事故逐漸成為造成人類傷亡的主要原因之一,其中57%的災難性事故與駕駛員疲勞駕駛有關。因此,加強疲勞駕駛檢測技術的研究,防止疲勞駕駛行為的發(fā)生,對提高道路交通安全具有十分重要的意義。本文主要研究基于信息融合的疲勞駕駛檢測方法,通過分析駕駛行為數(shù)據的變化特征來判斷駕駛員的駕駛狀態(tài)。首先,本文對國內外的研究現(xiàn)狀進行了廣泛調研,在總結前人研究的基礎上,介紹了駕駛行為與疲勞駕駛的關系以及疲勞駕駛的形成機理和表現(xiàn)特征。并利用模擬駕駛平臺開展駕駛實驗,設計并完成了疲勞駕駛和正常駕駛兩組實驗,采集了25名駕駛員在不同駕駛狀態(tài)下的駕駛行為數(shù)據,并對數(shù)據進行了整理與篩選,建立了疲勞駕駛樣本數(shù)據庫。其次,分析了駕駛員在不同駕駛狀態(tài)下的駕駛行為特征。運用統(tǒng)計分析法對駕駛行為參量的時間序列變化趨勢、均值和標準差進行了對比分析。并提出采用樣本熵對駕駛行為數(shù)據的復雜度進行分析。通過研究,明確了駕駛員疲勞駕駛時的操作行為和車輛運行狀態(tài)的變化特征,最終提取了速度、方向盤轉角和車輛橫向位置作為區(qū)分駕駛狀態(tài)的特征參量。再次,依據模式分類的基本原理,采用KNN方法建立了基于單參數(shù)的疲勞駕駛檢測算法,并引入DTW距離對算法進行了改進。研究表明,基于單參數(shù)的檢測算法對疲勞駕駛的識別準確率總體不高,但采用DTW距離改進算法的識別性能更好。最后,建立了基于信息融合的疲勞駕駛檢測算法。提出了一種改進的加權投票法對基于單參數(shù)的疲勞駕駛檢測算法進行了決策層融合。為與決策層融合方法進行對比,采用BP和GA_BP神經網絡對多個駕駛行為特征進行了特征層融合。通過對比分析各疲勞駕駛檢測算法的識別準確率與運行效率,發(fā)現(xiàn)基于加權投票法的融合算法和基于GA_BP神經網絡的融合算法的識別效果均較好,但前者的識別效果更優(yōu)。
[Abstract]:With the continuous growth of the number of motor vehicles in China, road traffic safety problems are becoming increasingly serious. Road traffic accidents have gradually become one of the main causes of human casualties, 57% of which are related to driver fatigue driving. Therefore, it is of great significance to strengthen the research of fatigue driving detection technology and prevent the occurrence of fatigue driving behavior to improve road traffic safety. In this paper, the fatigue driving detection method based on information fusion is studied, and the driver's driving state is judged by analyzing the changing characteristics of driving behavior data. First of all, this paper has carried on the extensive investigation to the domestic and foreign research present situation, has summarized the predecessor research foundation, has introduced the relationship between driving behavior and fatigue driving, as well as the fatigue driving formation mechanism and the performance characteristic. Using the simulated driving platform to carry out driving experiments, two groups of experiments of fatigue driving and normal driving are designed and completed. The driving behavior data of 25 drivers under different driving conditions are collected, and the data are sorted out and screened. A database of fatigue driving samples was established. Secondly, the characteristics of driver's driving behavior under different driving conditions are analyzed. The trend of time series, mean value and standard deviation of driving behavior parameters are analyzed by statistical analysis. Sample entropy is used to analyze the complexity of driving behavior data. Through the research, the operating behavior of the driver during fatigue driving and the changing characteristics of the vehicle running state are defined. Finally, the speed, steering wheel angle and the lateral position of the vehicle are extracted as the characteristic parameters to distinguish the driving state. Thirdly, according to the basic principle of pattern classification, the fatigue driving detection algorithm based on single parameter is established by using KNN method, and the DTW distance is introduced to improve the algorithm. The results show that the detection accuracy of fatigue driving based on single parameter detection algorithm is not high, but the performance of the improved algorithm based on DTW distance is better. Finally, a fatigue driving detection algorithm based on information fusion is established. An improved weighted voting method is proposed for decision level fusion of fatigue driving detection algorithm based on single parameter. In order to compare with the method of decision level fusion, BP and GA_BP neural networks are used to fuse the characteristics of multiple driving behaviors. By comparing and analyzing the recognition accuracy and running efficiency of each fatigue driving detection algorithm, it is found that the fusion algorithm based on weighted voting method and the fusion algorithm based on GA_BP neural network have better recognition effect, but the former has better recognition effect.
【學位授予單位】：北京工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.254;TP18

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本文編號：2278863