本文選題：駕駛行為操縱模式 + 主成分分析法(PCA)��； 參考：《合肥工業(yè)大學》2015年碩士論文

【摘要】：隨著現(xiàn)代社會的發(fā)展,車輛已經(jīng)成為人們?nèi)粘Ｋ豢苫蛉钡慕煌üぞ?然而,隨著汽車保有量的增加,相應的問題也與之而來,及交通事故發(fā)生率的增加,而在交通事故中,駕駛員這一因素的影響又至關重要,所以需要提高駕駛員的駕駛能力�；谝陨弦蛩�,出現(xiàn)了駕駛輔助系統(tǒng),他可以為駕駛員提供輔助和支持,在潛在危險出現(xiàn)時,可以為駕駛員提供報警信息以及對車輛的輔助控制。但是由于駕駛員個體之間的駕駛特性完全不一樣,座椅給輔助系統(tǒng)的設計造成了較大的困難,如何使系統(tǒng)在保證安全的前提下去適應駕駛員差異,成為系統(tǒng)設計的一個問題。 根據(jù)以上問題的出現(xiàn),本文中主要的研究內(nèi)容如下： (1)通過采集并分析駕駛行為與駕駛操作動作模式的數(shù)據(jù),利用主成分分析法研究駕駛行為及其對應的駕駛操縱動作模式之間的關系； (2)根據(jù)個體駕駛?cè)送瓿梢粋�具體駕駛行為的駕駛操縱動作具有一定的內(nèi)聚性、時序性和個性化的機理,研究構建基于有向圖的駕駛?cè)笋{駛行為操作模式建模方法； (3)根據(jù)前面利用主成分分析法提取出的對于各個駕駛行為占主要影響的駕駛操作動作特征數(shù)據(jù),設計針對不同特征主成分和各個特征向量組合的神經(jīng)網(wǎng)絡算法,可以反向?qū)崿F(xiàn)個性化駕駛行為的識別,進一步為利用駕駛行為及駕駛操縱動作模式設計先進的駕駛輔助系統(tǒng)提供技術支撐。 研究的結果表明： (1)主成分分析法可以確定出對于特定的駕駛行為影響較為關鍵的駕駛操作動作； (2)可以利用MATLAB仿真軟件構建基于有向圖模式的駕駛行為操作模式,可以用于建立機動車駕駛?cè)笋{駛行為操縱模型,有利于自適應系統(tǒng)的建立； (3)Fuzzy_ARTMAP神經(jīng)網(wǎng)絡駕駛行為識別算法具有較高的準確率以及較低的虛警率,該方法可以作為駕駛行為識別中一個有效可行的解決方案。
[Abstract]:With the development of modern society, vehicles have become an indispensable means of transportation for people. However, with the increase of vehicle ownership, the corresponding problems also arise, and the incidence of traffic accidents increases, and in traffic accidents, The driver's influence is also very important, so it is necessary to improve the driver's driving ability. Based on the above factors, there is a driving assistance system, which can provide assistance and support for the driver, and can provide alarm information and auxiliary control to the vehicle when the potential danger arises. However, since the driving characteristics of individual drivers are completely different, it is difficult for the seat to design the auxiliary system. How to make the system adapt to the drivers' differences on the premise of ensuring safety has become a problem in the system design. According to the above problems, the main contents of this paper are as follows: 1) by collecting and analyzing the data of driving behavior and driving operation mode, the relationship between driving behavior and its corresponding driving operation mode is studied by principal component analysis. 2) according to the mechanism that individual driver has certain cohesion, timing and individuation to complete a specific driving behavior, the modeling method of driving behavior operation mode based on directed graph is studied. (3) according to the characteristic data of driving operation, which is extracted by principal component analysis (PCA), a neural network algorithm is designed for different feature principal components and combination of each characteristic vector. It can realize the recognition of individual driving behavior in reverse direction, and provide technical support for the design of advanced driving assistant system by using driving behavior and driving manipulation mode. The results of the study show that: 1) Principal component analysis (PCA) can determine the key driving actions that affect the specific driving behavior. 2) the driving behavior operation mode based on directed graph mode can be constructed by using MATLAB simulation software, and it can be used to establish the driving behavior control model of motor vehicle, which is beneficial to the establishment of adaptive system. FuzzyARMAP neural network has high accuracy and low false alarm rate. This method can be used as an effective and feasible solution in driving behavior recognition.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.25;U463.6

【參考文獻】

相關期刊論文 前10條

1 ;2006年全國道路交通事故概況[J];道路交通管理;2007年02期

2 張德兆;王建強;李升波;李克強;;基于風險狀態(tài)預估的彎道防側(cè)滑超速預警系統(tǒng)[J];公路交通科技;2009年S1期

3 張開冉;;基于BP神經(jīng)網(wǎng)絡模型的駕駛行為環(huán)境影響評價[J];華北科技學院學報;2005年04期

4 宗長富;楊肖;王暢;張廣才;;汽車轉(zhuǎn)向時駕駛員駕駛意圖辨識與行為預測[J];吉林大學學報(工學版);2009年S1期

5 王曉原;楊新月;;駕駛行為非參數(shù)微觀仿真模型[J];交通運輸工程學報;2007年01期

6 張磊;王建強;楊馥瑞;李克強;;駕駛員行為模式的因子分析和模糊聚類[J];交通運輸工程學報;2009年05期

7 孫平;宋瑞;王海霞;;我國道路交通事故成因分析及預防對策[J];安全與環(huán)境工程;2007年02期

8 于黎明,王占林;人機系統(tǒng)最優(yōu)預見補償跟蹤控制研究[J];自動化學報;2001年03期

9 孫以澤,王其明;車輛AMT中道路條件及駕駛意圖的模糊識別[J];汽車工程;2001年06期

10 許駿;李一兵;;基于Markov決策過程的駕駛員行為模型[J];汽車工程;2008年01期

相關博士學位論文 前1條

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

，

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