本文關(guān)鍵詞： 主動安全 BP神經(jīng)網(wǎng)絡(luò) 碰撞預(yù)測 D-MAC協(xié)議　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,交通事故呈不斷上升的趨勢,尤其是高速公路上的追尾事故頻發(fā),因此人們越來越多的關(guān)注如何保障高速公路行車安全。目前保障汽車行駛安全的技術(shù)主要分為被動安全技術(shù)和主動安全技術(shù)。主動安全技術(shù)能夠根據(jù)當(dāng)前車輛的運(yùn)動狀態(tài)和周圍環(huán)境信息,對潛在的沖突予以判斷。主動安全技術(shù)能從源頭上抑制交通事故的發(fā)生,所以對汽車主動安全技術(shù)的研究具有重要的意義。車輛狀態(tài)預(yù)測是汽車主動安全技術(shù)的基礎(chǔ),根據(jù)以往車輛碰撞預(yù)測的成果,本文針對車輛碰撞概率的預(yù)測展開研究�，F(xiàn)有的車輛碰撞概率計(jì)算方法只是結(jié)合運(yùn)動學(xué)公式和車輛的分布情況對車輛追尾碰撞概率進(jìn)行計(jì)算,沒有考慮車輛追尾碰撞的真實(shí)場景,預(yù)測結(jié)果有偏差。為了提高車輛碰撞概率預(yù)測的準(zhǔn)確度,本文綜合考慮造成車輛碰撞的駕駛員、車輛、道路和環(huán)境等因素,采用BP神經(jīng)網(wǎng)絡(luò)的方法對車輛的追尾碰撞情況進(jìn)行預(yù)測。由于BP神經(jīng)網(wǎng)絡(luò)的初始化連接權(quán)值和閾值的選擇具有很大的隨機(jī)性,可能使BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練的結(jié)果陷入局部最優(yōu),而遺傳算法具有全局尋優(yōu)的能力,因此本文選用遺傳算法對BP神經(jīng)網(wǎng)絡(luò)的初始化連接權(quán)值和閾值進(jìn)行優(yōu)化,此外,為了改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的收斂速度,本文進(jìn)一步對BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)速率進(jìn)行改進(jìn)。最后利用MATLAB仿真工具對本文中的車輛狀態(tài)預(yù)測算法進(jìn)行性能驗(yàn)證,結(jié)果表明,本文提出的算法能較準(zhǔn)確的預(yù)測車輛下一時(shí)刻的碰撞概率。準(zhǔn)確預(yù)測到車輛發(fā)生碰撞的信息后,確保告警信息可靠及時(shí)的發(fā)送是十分必要的。為了保障安全信息的可靠及時(shí)的傳輸,同時(shí)提高周期性beacon消息接入信道的公平性。本文針對高速公路場景的車輛協(xié)同防撞應(yīng)用,根據(jù)以RSU為中心的車隊(duì)中車輛節(jié)點(diǎn)的數(shù)目和車隊(duì)中發(fā)生潛在碰撞的車輛節(jié)點(diǎn)的數(shù)目,設(shè)計(jì)了保障告警信息可靠及時(shí)傳輸?shù)腄-MAC(Dynamic slot Media Access Control,D-MAC)協(xié)議。該協(xié)議是基于動態(tài)TDMA機(jī)制,根據(jù)當(dāng)前車隊(duì)中的實(shí)時(shí)交通動態(tài)確定每幀的時(shí)隙數(shù)目,并且該協(xié)議優(yōu)先為告警信息分配時(shí)隙,同時(shí)盡可能地為周期性beacon消息預(yù)留傳輸時(shí)隙。最后,在不同的車輛節(jié)點(diǎn)密度的情況下,對本文提出的動態(tài)介質(zhì)訪問控制協(xié)議的性能進(jìn)行評估,與IEEE802.11p協(xié)議相比,D-MAC協(xié)議的投遞率增加了大約39%,在節(jié)點(diǎn)密度較大時(shí),和IEEE802.11p協(xié)議相比,D-MAC協(xié)議的數(shù)據(jù)傳輸時(shí)延更低,并且D-MAC協(xié)議獲取無線信道服務(wù)的公平性提高了31%。
[Abstract]:In recent years, traffic accidents have been on the rise, especially the frequent rear-end accidents on highways. Therefore, people are paying more and more attention to how to ensure the safety of motorway. At present, the technology to ensure the safety of vehicle driving is mainly divided into passive safety technology and active safety technology. Active safety technology can be based on the current vehicle. State of motion and surrounding environment information, The active safety technology can restrain the occurrence of the traffic accident from the source, so the research on the active safety technology of the automobile is of great significance. The vehicle state prediction is the basis of the active safety technology of the vehicle. According to the results of vehicle collision prediction in the past, this paper studies the prediction of vehicle collision probability. The existing methods of calculating vehicle collision probability are only based on kinematics formula and vehicle distribution to calculate the rear-end collision probability. In order to improve the accuracy of the prediction of vehicle collision probability, the driver, vehicle, road and environment of vehicle collision are considered comprehensively in this paper. The method of BP neural network is used to predict the rear-end collision of the vehicle. Because of the randomness of the selection of the initial connection weight and the threshold value of BP neural network, the result of BP neural network training may fall into local optimum. The genetic algorithm has the ability of global optimization, so the genetic algorithm is used to optimize the initial connection weight and threshold of BP neural network. In addition, in order to improve the convergence speed of BP neural network, In this paper, the learning rate of BP neural network is further improved. Finally, the performance of the vehicle state prediction algorithm in this paper is verified by using MATLAB simulation tool, and the results show that, The algorithm proposed in this paper can accurately predict the collision probability of vehicles at the next moment. It is very necessary to ensure the reliable and timely transmission of alarm information. In order to ensure the reliable and timely transmission of security information and to improve the fairness of periodic beacon message access channel, this paper aims at the vehicle anti-collision application of freeway scene. According to the number of vehicle nodes in the vehicle fleet centered on RSU and the number of vehicle nodes with potential collision in the vehicle fleet, a D-MAC dynamic slot Media Access Control D-MACCprotocol is designed to guarantee the reliable and timely transmission of alarm information. The protocol is based on the dynamic TDMA mechanism. The number of time slots per frame is determined according to the real-time traffic dynamics in the current motorcade, and the protocol preferentially allocates time slots for alarm information, while reserving transmission slots for periodic beacon messages as far as possible. Finally, In the case of different vehicle node density, the performance of the proposed dynamic media access control protocol is evaluated. Compared with the IEEE802.11p protocol, the delivery rate of the D-MAC protocol increases by about 39 percent. Compared with the IEEE802.11p protocol, the data transmission delay of the D-MAC protocol is lower, and the fairness of the D-MAC protocol to obtain wireless channel services is improved by 31.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 呂婷婷;錢勇生;曾俊偉;;中國和美國道路交通事故數(shù)據(jù)對比研究[J];城市道橋與防洪;2015年10期

2 任宇立;;道路交通安全現(xiàn)狀及交通事故分析方法初探[J];道路交通與安全;2010年02期

3 馬社強(qiáng);劉東;鄭英力;;中國道路交通事故影響因素及對策[J];中國人民公安大學(xué)學(xué)報(bào)(自然科學(xué)版);2007年03期

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本文編號：1544128