本文關(guān)鍵詞：DSRC與車載傳感器融合的智能車輛目標跟蹤方法研究　出處：《重慶郵電大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

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【摘要】：智能車輛目標跟蹤系統(tǒng)用于對周圍目標進行實時跟蹤,準確地獲取目標狀態(tài)信息是安全預(yù)警、輔助駕駛和自動駕駛等系統(tǒng)必要的前提條件。在協(xié)同式智能車輛場景中,傳統(tǒng)的車輛跟蹤方法單純地依靠車載傳感器對目標進行跟蹤,沒有充分利用通過車間協(xié)同方式得到的信息,存在數(shù)據(jù)關(guān)聯(lián)和跟蹤濾波結(jié)果準確性不高的問題,因此,研究利用通過車間協(xié)同方式獲取信息的目標跟蹤方法具有重要的意義。本文針對現(xiàn)有的車輛目標跟蹤方法在協(xié)同式智能車輛場景中存在的不足,提出一種利用專用短程通信技術(shù)(Dedicated Short Range Communications,DSRC)與車載傳感器融合的智能車輛目標跟蹤方法。在協(xié)同式智能車輛場景下,本文方法考慮了協(xié)同目標車輛可通過DSRC通信技術(shù)發(fā)布其自身的狀態(tài)及身份等信息,利用這部分信息對數(shù)據(jù)關(guān)聯(lián)和跟蹤濾波的結(jié)果進行修正,有效的提高了數(shù)據(jù)關(guān)聯(lián)準確性和跟蹤精度。本文方法的基本思路是:首先主車通過激光雷達傳感器采集數(shù)據(jù)并對數(shù)據(jù)進行初步處理,利用數(shù)據(jù)關(guān)聯(lián)算法對激光雷達的觀測結(jié)果進行數(shù)據(jù)關(guān)聯(lián);其次,通過DSRC通信設(shè)備接收并處理協(xié)同目標車輛發(fā)布的自身狀態(tài)及身份等信息,利用處理后的DSRC信息對數(shù)據(jù)關(guān)聯(lián)的結(jié)果進行修正;最后對修正后的數(shù)據(jù)關(guān)聯(lián)結(jié)果進行跟蹤濾波并與目標車輛發(fā)布的狀態(tài)信息進行融合。通過仿真對比,驗證了本文方法的有效性。本文將提出的跟蹤方法應(yīng)用到協(xié)同式智能車輛場景的目標跟蹤中,設(shè)計并開發(fā)了智能車輛目標跟蹤系統(tǒng)。該系統(tǒng)主要包含數(shù)據(jù)采集處理和目標跟蹤兩大模塊,數(shù)據(jù)采集處理模塊負責(zé)完成對各車載傳感器的數(shù)據(jù)采集與處理,將處理后的數(shù)據(jù)傳給車輛目標跟蹤模塊進行目標跟蹤,最后將跟蹤等結(jié)果通過人機交互界面實時顯示。本文設(shè)計實現(xiàn)的智能車輛目標跟蹤系統(tǒng)按照高內(nèi)聚,低耦合的思想對軟件進行模塊化處理,使得該系統(tǒng)具有良好的可擴展性。最后,對本文開發(fā)的智能車輛目標跟蹤系統(tǒng)進行了實車實驗,通過實驗結(jié)果驗證了本文方法和系統(tǒng)的有效性。
[Abstract]:The intelligent vehicle target tracking system is used for real-time tracking of the surrounding targets, and accurately obtain the information of the target status. It is a necessary prerequisite for such systems as safety early warning, auxiliary driving and automatic driving. In Cooperative Intelligent Vehicle in the scene, the traditional vehicle tracking method simply rely on on-board sensors to track the target, did not make full use of the information obtained through the way of collaborative workshop, existing data association and tracking filtering accuracy is not high, therefore, has an important significance to research the use of tracking method to obtain information through the way of collaborative workshop the target. Aiming at the shortcomings of existing vehicle target tracking methods in collaborative intelligent vehicle scene, this paper proposes an intelligent vehicle tracking method based on fusion of Dedicated Short Range Communications (DSRC) and vehicle sensors. In Cooperative Intelligent Vehicle scene, this method considers the cooperative target vehicle can be released and its own identity and other information through the DSRC communication technology, the use of this part of the information on the data association and tracking filter was modified, and effectively improve the accuracy of the data association and tracking accuracy. The basic idea of this method is: firstly, the main vehicle through the data acquisition of laser radar sensor and preliminary data processing, observation of laser radar data correlation based data association algorithm; secondly, through its own status and identity information such as DSRC communication equipment for receiving and processing the vehicle collaborative target release, the use of DSRC after treatment the results of data association information is modified; at the end of the data association after the correction of state information tracking and released with the target vehicle integration. The effectiveness of this method is verified by simulation comparison. In this paper, the proposed tracking method is applied to the target tracking of collaborative intelligent vehicle scene, and the intelligent vehicle target tracking system is designed and developed. The system includes data acquisition processing and target tracking of two modules, data acquisition module is responsible for data acquisition and processing of the vehicle sensor, the processed data is transmitted to the vehicle target tracking module of target tracking, the tracking results through the man-machine interface Real-Time display. The intelligent vehicle tracking system designed and implemented in this paper modularized the software according to the idea of high cohesion and low coupling, enabling the system to have good expansibility. Finally, the actual vehicle experiment is carried out on the intelligent vehicle target tracking system developed in this paper, and the effectiveness of the method and system is verified by the experimental results.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U463.6;TP212

【參考文獻】

相關(guān)期刊論文 前10條

1 王曉君;裴福俊;劉紅云;;一種改進馬氏距離的最近鄰數(shù)據(jù)關(guān)聯(lián)算法[J];導(dǎo)航定位學(xué)報;2015年04期

2 李翠蕓;王榮;姬紅兵;;基于變分貝葉斯勢均衡多目標多伯努利濾波的多擴展目標跟蹤算法[J];控制理論與應(yīng)用;2015年02期

3 張宇;;一種圖像確認目標的多目標跟蹤方法[J];電子測量與儀器學(xué)報;2014年06期

4 賀豐收;繆禮鋒;;一種交互式多模型全局最近鄰關(guān)聯(lián)算法[J];火力與指揮控制;2013年10期

5 李萬高;趙雪梅;孫德廠;;基于改進貝葉斯方法的軌跡預(yù)測算法研究[J];計算機應(yīng)用;2013年07期

6 童慧思;張顥;孟華東;王希勤;;PHD濾波器在多目標檢測前跟蹤中的應(yīng)用[J];電子學(xué)報;2011年09期

7 陳超;呂植勇;付姍姍;彭琪;;國內(nèi)外車路協(xié)同系統(tǒng)發(fā)展現(xiàn)狀綜述[J];交通信息與安全;2011年01期

8 劉楓;吳小俊;;一種改進的聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法[J];數(shù)據(jù)采集與處理;2011年01期

9 甘志梅;王春香;楊明;;基于激光雷達的車輛跟蹤與識別方法[J];上海交通大學(xué)學(xué)報;2009年06期

10 李舜酩;沈\，

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