本文關(guān)鍵詞：智能車環(huán)境下車輛典型行為識(shí)別方法研究　出處：《長(zhǎng)安大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 智能車輛 環(huán)境感知 行為識(shí)別 車輛姿態(tài)估計(jì) 車輛速度估計(jì) 支持向量機(jī)

【摘要】：智能車輛的行為識(shí)別作為智能車輛環(huán)境感知的重要組成部分,能夠?yàn)橹悄苘囕v的控制和決策提供必要的數(shù)據(jù)支撐,是智能車輛安全、可靠運(yùn)行的前提和基礎(chǔ)。論文針對(duì)智能車輛典型行為識(shí)別問題,搭建了一種智能車輛典型行為識(shí)別系統(tǒng),并在此基礎(chǔ)上對(duì)智能車輛的典型行為識(shí)別方法進(jìn)行研究。主要工作包括:(1)在對(duì)智能車輛體系結(jié)構(gòu)和功能結(jié)構(gòu)進(jìn)行研究的基礎(chǔ)上,構(gòu)建了一種智能車輛典型行為識(shí)別系統(tǒng),進(jìn)而分析了該系統(tǒng)中各個(gè)模塊的工作原理及其涉及的關(guān)鍵技術(shù),并完成了系統(tǒng)的硬件選擇。(2)在對(duì)智能車輛姿態(tài)估計(jì)算法進(jìn)行深入研究的基礎(chǔ)上,選擇旋轉(zhuǎn)矢量法作為陀螺儀的車輛姿態(tài)估計(jì)方法,選擇高斯牛頓法作為加速度計(jì)和磁強(qiáng)計(jì)的車輛姿態(tài)估計(jì)方法,然后使用擴(kuò)展卡爾曼濾波算法對(duì)兩種估計(jì)方法的結(jié)果進(jìn)行融合,實(shí)現(xiàn)了車輛姿態(tài)估計(jì);通過仿真實(shí)驗(yàn)驗(yàn)證了該方法能夠?qū)崿F(xiàn)較好的車輛姿態(tài)估計(jì)效果。(3)針對(duì)車輛加速度積分求取車輛速度時(shí)的累積誤差問題,提出了一種基于加速度修正的智能車輛速度估計(jì)方法。通過仿真實(shí)驗(yàn)驗(yàn)證了該方法能夠有效減少車輛加速度誤差和噪聲對(duì)速度估計(jì)的影響,具有較好的車輛速度估計(jì)效果。(4)通過對(duì)智能車輛典型行為中車輛姿態(tài)和速度表現(xiàn)特點(diǎn)的分析,對(duì)車輛典型行為類型進(jìn)行了劃分;根據(jù)不同車輛行為類型的特點(diǎn),分別提取車輛速度曲線的數(shù)字特征和車輛姿態(tài)信號(hào)的FFT系數(shù)作為車輛行為的特征,并將其作為支持向量機(jī)算法的輸入量實(shí)現(xiàn)了智能車輛典型行為的識(shí)別。(5)搭建了一種智能車輛典型行為識(shí)別測(cè)試平臺(tái),完成了智能車輛典型行為識(shí)別軟件和算法的設(shè)計(jì),通過實(shí)驗(yàn)驗(yàn)證了所提出的智能車輛典型行為識(shí)別方法的有效性。
[Abstract]:As an important part of intelligent vehicle environment awareness, intelligent vehicle behavior recognition can provide necessary data support for intelligent vehicle control and decision-making, which is the security of intelligent vehicle. The premise and foundation of reliable operation. Aiming at the problem of intelligent vehicle typical behavior recognition, a typical behavior recognition system of intelligent vehicle is built in this paper. On this basis, the typical behavior recognition method of intelligent vehicle is studied. The main work includes: 1) on the basis of the research of intelligent vehicle architecture and function structure. In this paper, a typical behavior recognition system for intelligent vehicles is constructed, and then the working principle of each module and the key technologies involved in the system are analyzed. And completed the hardware selection of the system. 2) on the basis of the in-depth research on the intelligent vehicle attitude estimation algorithm, the rotation vector method is selected as the gyro vehicle attitude estimation method. Gao Si Newton method is chosen as the vehicle attitude estimation method of accelerometer and magnetometer, and the results of the two methods are fused by using extended Kalman filter algorithm to realize vehicle attitude estimation. The simulation results show that this method can achieve better vehicle attitude estimation effect. An intelligent vehicle speed estimation method based on acceleration correction is proposed. The simulation results show that the method can effectively reduce the impact of vehicle acceleration error and noise on speed estimation. By analyzing the characteristics of vehicle attitude and speed in the typical behavior of intelligent vehicles, the typical behavior types of vehicles are divided. According to the characteristics of different vehicle behavior types, the digital characteristics of vehicle velocity curve and the FFT coefficient of vehicle attitude signal are extracted as the characteristics of vehicle behavior. It is used as input of support vector machine algorithm to realize the recognition of typical behavior of intelligent vehicle. 5) A test platform for recognition of typical behavior of intelligent vehicle is built. The software and algorithm of intelligent vehicle canonical behavior recognition are designed, and the effectiveness of the proposed method is verified by experiments.
【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)】：U495;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 黃巖;吳軍;劉春明;李兆斌;;自主車輛發(fā)展概況及關(guān)鍵技術(shù)[J];兵工自動(dòng)化;2010年11期

2 夏顯峰;王Y，

本文編號(hào)：1377477