本文選題：車適應(yīng)人 + 駕駛員行為特性��； 參考：《吉林大學(xué)》2015年碩士論文

【摘要】：傳統(tǒng)汽車控制系統(tǒng)由于受到汽車機(jī)械結(jié)構(gòu)的限制,汽車的潛能不能很好發(fā)揮。隨著車載網(wǎng)絡(luò)、微處理器等技術(shù)的迅速發(fā)展,很多研究機(jī)構(gòu)和汽車廠家都將線控技術(shù)運(yùn)用到了汽車上。汽車線控技術(shù)減少了液壓、機(jī)械控制裝置等部件,降低了整車質(zhì)量,方便了電線布置,由于線控系統(tǒng)控制算法的靈活多變和系統(tǒng)參數(shù)可調(diào),線控汽車的動(dòng)力學(xué)控制比傳統(tǒng)汽車具有更大的發(fā)展空間,線控汽車已成為國(guó)內(nèi)外研究熱點(diǎn)。將駕駛員的特性考慮到車輛集成控制的設(shè)計(jì)中,就可以實(shí)現(xiàn)人性化設(shè)計(jì),變“人適應(yīng)車”的現(xiàn)狀為“車適應(yīng)人”。另外,汽車上各種電控系統(tǒng)的應(yīng)用及電控系統(tǒng)的集成控制,提高了汽車電子化和智能化水平,在汽車的主動(dòng)安全性和駕駛舒適性方面發(fā)揮了越來(lái)越重要的作用。為了提高不同駕駛員對(duì)駕駛輔助系統(tǒng)的接受度,在設(shè)計(jì)控制算法時(shí)需要考慮駕駛員的特性,在保證汽車安全性和駕駛舒適性的前提下,通過(guò)對(duì)駕駛員特性的辨識(shí)實(shí)現(xiàn)汽車對(duì)駕駛員的自適應(yīng)和駕駛員個(gè)性化駕駛。因此無(wú)論是要實(shí)現(xiàn)“車適應(yīng)人”線控汽車,還是提高駕駛員對(duì)駕駛輔助系統(tǒng)的接受度,都需要對(duì)駕駛員特性進(jìn)行辨識(shí)。 本文依托于吉林大學(xué)汽車仿真與控制國(guó)家重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目‘'Integrated Control Method for a Full Drive-by-Wire Electric Vehicle Based on Driver's Intention Recognition"(基金編號(hào)：20120111)、中國(guó)博士后科學(xué)基金資助項(xiàng)目(項(xiàng)目編號(hào)：2014M561289)和國(guó)家自然科學(xué)基金青年基金資助項(xiàng)目(項(xiàng)目編號(hào)：51305190),以建立一套適用于線控汽車底盤動(dòng)力學(xué)控制系統(tǒng)的駕駛員行為特性辨識(shí)算法為目標(biāo),搭建駕駛模擬器并選用多名駕駛員進(jìn)行試驗(yàn),從采集到的試驗(yàn)數(shù)據(jù)中提取特征參數(shù)并利用K-means進(jìn)行聚類,獲得駕駛員行為特性的類型和數(shù)據(jù)樣本,以此為基礎(chǔ),建立了基于神經(jīng)網(wǎng)絡(luò)的駕駛員行為特性辨識(shí)模型,并通過(guò)駕駛模擬器試驗(yàn)對(duì)模型的精度和預(yù)測(cè)能力進(jìn)行了驗(yàn)證。 為了建立駕駛員行為特性辨識(shí)模型,本文主要進(jìn)行了以下工作： (1)搭建駕駛模擬器 為了充分挖掘駕駛員的行為特性,需要進(jìn)行不同工況下的大量實(shí)驗(yàn),由于實(shí)車的可調(diào)參數(shù)少,以及受環(huán)境限制所能選取的工況有限,因此,本文在課題組已有研究基礎(chǔ)上,搭建駕駛模擬器進(jìn)行試驗(yàn),獲取數(shù)據(jù)樣本,為建立駕駛員行為特性辨識(shí)模型做準(zhǔn)備。本文在提出駕駛模擬器的總體框架和工作原理基礎(chǔ)上,詳細(xì)介紹了駕駛模擬器主體、模擬器操控臺(tái)、車輛動(dòng)力學(xué)仿真模型、實(shí)時(shí)仿真系統(tǒng)、轉(zhuǎn)向力感模擬系統(tǒng)和傳感器系統(tǒng)等關(guān)鍵組成部分。 (2)對(duì)駕駛員行為特性進(jìn)行分類 本文總結(jié)并分析了駕駛員行為特性分類的方法,選用K-means算法對(duì)駕駛員行為特性進(jìn)行分類；基于已搭建的駕駛模擬器,設(shè)計(jì)了轉(zhuǎn)向、制動(dòng)、加速試驗(yàn)工況,選用13名試驗(yàn)人員進(jìn)行試驗(yàn)并采集數(shù)據(jù)；通過(guò)對(duì)駕駛員轉(zhuǎn)向、制動(dòng)、加速行為進(jìn)行分析,選取表征駕駛員轉(zhuǎn)向、制動(dòng)、加速行為特性的特征參數(shù),并利用MATLAB編程從試驗(yàn)數(shù)據(jù)中提取特征參數(shù)；基于K-means算法對(duì)特征參數(shù)進(jìn)行聚類,進(jìn)而將駕駛員的轉(zhuǎn)向、制動(dòng)、加速行為特性分別分為謹(jǐn)慎型、一般型和激進(jìn)型,同時(shí)獲得每個(gè)類型的數(shù)據(jù)樣本,為搭建駕駛員行為特性辨識(shí)模型提供數(shù)據(jù)。 (3)建立駕駛員行為特性辨識(shí)模型 由于駕駛員特性辨識(shí)就是對(duì)駕駛員特性這一模式進(jìn)行識(shí)別的過(guò)程,因此本文介紹了幾種常用的模式識(shí)別方法并分析對(duì)比了它們各自的優(yōu)缺點(diǎn),以及這些模式識(shí)別方法用于駕駛員行為特性辨識(shí)時(shí)的優(yōu)缺點(diǎn)和適用范圍,確定選取BP神經(jīng)網(wǎng)絡(luò)作為駕駛員行為特性辨識(shí)模型的建模方法。本文利用駕駛員行為分類中所得到的各個(gè)類型的數(shù)據(jù)樣本,建立了基于BP神經(jīng)網(wǎng)絡(luò)的駕駛員行為特性辨識(shí)模型,針對(duì)BP神經(jīng)網(wǎng)絡(luò)輸入輸出變量的選取、網(wǎng)絡(luò)結(jié)構(gòu)的設(shè)計(jì)以及網(wǎng)絡(luò)的訓(xùn)練過(guò)程進(jìn)行了詳細(xì)說(shuō)明,最后利用駕駛模擬器試驗(yàn)對(duì)模型精度及預(yù)測(cè)能力進(jìn)行了驗(yàn)證。
[Abstract]:As the traditional automobile control system is limited by the mechanical structure of automobile, the potential of automobile can not be exerted very well. With the rapid development of the technology of vehicle network and microprocessor, many research institutions and automobile manufacturers have applied the wire control technology to the car. The automobile wire control technology has reduced the hydraulic and mechanical control devices and so on. The quality of the whole vehicle is convenient for the layout of the wire. Because of the flexibility of the control algorithm and the adjustable parameters of the system, the dynamic control of the line control car has a greater development space than the traditional car. The line control car has become a hot spot of research at home and abroad. In addition, the application of various electronic control systems on the car and the integrated control of the electronic control system have improved the electronic and intelligent level of the automobile, and played a more and more important role in the vehicle's active safety and driving comfort. The acceptance of the driving auxiliary system needs to consider the driver's characteristics in the design of the control algorithm. On the premise of ensuring the safety and driving comfort of the car, the driver's self adaptation and driver's individualized driving are realized by the identification of the driver's characteristics. To improve driver's acceptance of driver assistance system, we need to identify driver's characteristics.
This paper is based on the open fund project of the State Key Laboratory of automobile simulation and control of Jilin University, "'Integrated Control Method for a Full Drive-by-Wire Electric Vehicle Based on Driver's" (fund number: 20120111), China Post Doctoral Science Fund funded project (project number:) and country The project (project number: 51305190) of the National Natural Science Fund Youth Fund (project number: 51305190) aims to establish a set of driver behavior identification algorithms suitable for the dynamic control system of the car chassis, build a driving simulator and choose a number of drivers to carry out the test, extract the characteristic parameters from the collected test data and use the K-me Ans is used to cluster, and the type of driver behavior and data samples are obtained. Based on this, the identification model of driver behavior based on neural network is established, and the accuracy and prediction ability of the model is verified by driving simulator test.
In order to establish driver behavior characteristic identification model, the following work is done in this paper.
(1) build driving simulator
In order to fully excavate the behavior characteristics of the driver, it is necessary to carry out a large number of experiments under different working conditions, because the adjustable parameters of the real car are few, and the working conditions can be limited by the environment restriction. Therefore, on the basis of the existing research group, this paper builds a driving simulator to test and obtain the data samples, in order to establish the identification of the driver's behavior characteristics. On the basis of the overall framework and working principle of driving simulator, this paper introduces the key components of driving simulator, simulator console, vehicle dynamics simulation model, real time simulation system, steering sense simulation system and sensor system.
(2) classify the behavior of the driver
This paper summarizes and analyzes the method of driver behavior classification, and uses K-means algorithm to classify the behavior characteristics of drivers. Based on the built driving simulator, the steering, braking, acceleration test conditions are designed, 13 experimenters are selected to test and collect data, and the driver's steering, braking, and acceleration behavior are carried out. Analysis, select characteristic parameters that characterizing driver's steering, braking, and acceleration behavior, and use MATLAB programming to extract characteristic parameters from the test data. Based on K-means algorithm, the characteristic parameters are clustered, and then the driver's steering, braking, and accelerating behavior characteristics are divided into discreet, general and radical, and each of them is obtained at the same time. The data samples provide data for building driver behavior identification model.
(3) establish the identification model of driver's behavior
As driver characteristic identification is the process of identifying the driver's characteristics, this paper introduces several common pattern recognition methods and analyzes their respective advantages and disadvantages, and the advantages and disadvantages of these pattern recognition methods used in the identification of drivers' behavior characteristics and the selection of BP neural network. As a modeling method for the identification model of driver's behavior characteristics, this paper sets up a driver behavior identification model based on BP neural network based on all types of data samples obtained in the driver's behavior classification. It aims at the selection of input and output variables of the BP neural network, the design of network structure and the training process of the network. The driving simulator test is used to verify the accuracy and prediction ability of the model.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.25;U463.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 宗長(zhǎng)富;林娜;張澤星;李剛;;線控汽車駕駛員特性辨識(shí)算法的研究[J];汽車工程;2014年09期

2 宗長(zhǎng)富;李剛;鄭宏宇;何磊;張澤星;;線控汽車底盤控制技術(shù)研究進(jìn)展及展望[J];中國(guó)公路學(xué)報(bào);2013年02期

3 宗長(zhǎng)富;林娜;李剛;張澤星;程衛(wèi);鄭宏宇;劉明輝;;“車適應(yīng)人”線控汽車?yán)硐胩匦詤⒖寄Ｐ蜕窠?jīng)網(wǎng)絡(luò)建模[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2013年S1期

4 趙曉華;房瑞雪;榮建;毛科俊;;基于生理信號(hào)的駕駛疲勞綜合評(píng)價(jià)方法試驗(yàn)研究[J];北京工業(yè)大學(xué)學(xué)報(bào);2011年10期

5 呂岸;胡振程;陳慧;;基于高斯混合隱馬爾科夫模型的高速公路超車行為辨識(shí)與分析[J];汽車工程;2010年07期

6 王建強(qiáng);遲瑞娟;張磊;李克強(qiáng);于濤;;適應(yīng)駕駛員特性的汽車追尾報(bào)警-避撞算法研究[J];公路交通科技;2009年S1期

7 吳堅(jiān);趙健;徐斌;李靜;李幼德;;基于dSPACE的汽車驅(qū)動(dòng)力控制系統(tǒng)硬件在環(huán)研究[J];汽車技術(shù);2009年09期

8 司傳勝;;奔馳E系列轎車SBC系統(tǒng)的功能特點(diǎn)[J];汽車維護(hù)與修理;2009年06期

9 宗長(zhǎng)富;楊肖;王暢;張廣才;;汽車轉(zhuǎn)向時(shí)駕駛員駕駛意圖辨識(shí)與行為預(yù)測(cè)[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2009年S1期

10 耿冠宏;孫偉;羅培;;神經(jīng)網(wǎng)絡(luò)模式識(shí)別[J];軟件導(dǎo)刊;2008年10期

相關(guān)博士學(xué)位論文 前2條

1 王博;四輪獨(dú)立電驅(qū)動(dòng)車輛實(shí)驗(yàn)平臺(tái)及驅(qū)動(dòng)力控制系統(tǒng)研究[D];清華大學(xué);2009年

2 張磊;基于駕駛員特性自學(xué)習(xí)方法的車輛縱向駕駛輔助系統(tǒng)[D];清華大學(xué);2009年

，

本文編號(hào)：1826122