發(fā)布時(shí)間：2018-06-06 04:27

  本文選題：跟車模型 + 神經(jīng)網(wǎng)絡(luò)��； 參考：《長(zhǎng)安大學(xué)》2015年碩士論文

【摘要】：車輛的跟馳行為是車輛行駛中的常見駕駛行為之一,特定的駕駛?cè)擞捎谄涓囘^(guò)程中心理感知等因素的差異,其行車車距和相對(duì)速度等安全范圍均不同。如果能夠?qū)︸{駛?cè)说倪@種跟車特性進(jìn)行模擬,建立起特定的跟車模型,就可以比較相似跟車狀態(tài)下的駕駛?cè)说母囆袨槭欠翊嬖诋惓�。采取相�?yīng)的措施對(duì)危險(xiǎn)的跟車行為及時(shí)進(jìn)行預(yù)警,就能夠有效降低事故的發(fā)生率。本文通過(guò)實(shí)際道路跟車試驗(yàn),使用視頻監(jiān)控系統(tǒng)和毫米波雷達(dá)等試驗(yàn)設(shè)備對(duì)車輛跟車過(guò)程中的相關(guān)跟車數(shù)據(jù)進(jìn)行采集。通過(guò)分析穩(wěn)定跟車過(guò)程中影響駕駛員進(jìn)行加減速操作的相關(guān)數(shù)據(jù),進(jìn)而基于篩選出有效的試驗(yàn)數(shù)據(jù)進(jìn)行預(yù)測(cè)模型研究。本文的主要研究?jī)?nèi)容和結(jié)論:(1)通過(guò)對(duì)跟車模型的相關(guān)研究回顧,提取試驗(yàn)數(shù)據(jù)中與駕駛員進(jìn)行加減速操作相關(guān)的車輛運(yùn)行參數(shù)和道路環(huán)境數(shù)據(jù)等。分析初步得出車輛相對(duì)速度、相對(duì)距離和本車速度可作為模型的特征輸入?yún)?shù)。(2)通過(guò)對(duì)初步提取的特征參數(shù)數(shù)據(jù)進(jìn)行處理,建立起B(yǎng)P神經(jīng)網(wǎng)絡(luò)跟車模型。通過(guò)預(yù)測(cè)分析可知BP模型很容易陷入局部極值點(diǎn),而這種問(wèn)題不能通過(guò)其自身結(jié)構(gòu)的優(yōu)化解決,因此考慮使用遺傳算法來(lái)對(duì)其進(jìn)行優(yōu)化。(3)遺傳算法對(duì)BP神經(jīng)網(wǎng)絡(luò)模型進(jìn)行結(jié)構(gòu)優(yōu)化后,結(jié)果表明以車輛相對(duì)速度、相對(duì)距離和本車速度為組合的跟車行為預(yù)測(cè)模型準(zhǔn)確度最高,但也僅為90.29%。通過(guò)反復(fù)試驗(yàn)可知經(jīng)過(guò)遺傳算法優(yōu)化后的雙隱含層的BP神經(jīng)網(wǎng)絡(luò)能夠?qū)㈩A(yù)測(cè)準(zhǔn)確率提高到94.17%。結(jié)果表明遺傳算法優(yōu)化后的雙隱含層BP神經(jīng)網(wǎng)絡(luò)跟車模型,能夠?qū)囕v的跟車狀態(tài)進(jìn)行很好地預(yù)測(cè)。本研究得到了教育部長(zhǎng)江學(xué)者與創(chuàng)新團(tuán)隊(duì)支持計(jì)劃項(xiàng)目(IRT1286)和交通運(yùn)輸部應(yīng)用基礎(chǔ)研究項(xiàng)目(2013319812150)的資助。
[Abstract]:Car-following behavior is one of the common driving behaviors in vehicle driving. Because of the difference of psychological perception in the process of following the vehicle, the safety range of driving distance and relative speed are different. If we can simulate the characteristics of the driver and set up a specific model, we can compare whether the behavior of the driver in the similar state is abnormal or not. It can effectively reduce the incidence of accidents by taking corresponding measures to warn the dangerous following behavior in time. In this paper, we use video surveillance system and millimeter-wave radar to collect the data of vehicle following through the actual road following test. Through the analysis of the relevant data which affect the driver's acceleration and deceleration operation in the process of stable following the vehicle, the prediction model is studied based on the screening of effective test data. The main contents and conclusions of this paper are as follows: (1) based on the review of the related research on the following model, the vehicle operation parameters and the road environment data related to the driver's acceleration and deceleration operation are extracted from the test data. The relative speed, relative distance and vehicle speed of the vehicle can be regarded as the characteristic input parameters of the model. The BP neural network model is established by processing the data of the initial extracted characteristic parameters. The prediction analysis shows that BP model is easy to fall into local extremum, but this problem can not be solved by optimizing its own structure. Therefore, considering the use of genetic algorithm to optimize the structure of BP neural network model, the results show that the combination of vehicle relative speed, relative distance and vehicle speed is the most accurate model for prediction of car-following behavior. But only 90.29. Through repeated experiments, it can be seen that the BP neural network with double hidden layers optimized by genetic algorithm can improve the prediction accuracy to 94.17%. The results show that the double hidden layer BP neural network model, which is optimized by genetic algorithm, can well predict the vehicle following state. This study was supported by the Ministry of Education's Yangtze River Scholars and Innovation team support Project (IRT1286) and the Ministry of Transport's Applied basic Research Project (2013319812150).
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.255;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 張磊;李升波;王建強(qiáng);李克強(qiáng);;基于神經(jīng)網(wǎng)絡(luò)方法的集成式駕駛員跟車模型[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年11期

2 賈洪飛,雋志才,王曉原;基于模糊推斷的車輛跟馳模型[J];中國(guó)公路學(xué)報(bào);2001年02期

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本文編號(hào)：1985090