本文關(guān)鍵詞： 傳感器 數(shù)據(jù)融合 駕駛行為 D-S證據(jù)理論　出處：《西安工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：為了提高道路交通安全,現(xiàn)代的無(wú)人車系統(tǒng)中廣泛采用各類傳感器來(lái)獲得車輛行駛所需要的環(huán)境信息。面對(duì)逐漸復(fù)雜的實(shí)際環(huán)境,傳統(tǒng)的單一傳感器由于不能滿足實(shí)時(shí)、快速地提供具有高精度和高可靠性的監(jiān)測(cè)與定位信息,同時(shí)又不具備自主性好、對(duì)環(huán)境變化的適應(yīng)能力強(qiáng)、抗干擾性強(qiáng)以及高性能價(jià)格比等要求,從而影響無(wú)人車行駛策略的制定。我們知道良好行駛策略的制定需要通過(guò)大量的環(huán)境信息對(duì)車輛關(guān)鍵部位行駛狀態(tài)進(jìn)行監(jiān)測(cè),進(jìn)而能夠更好地了解整個(gè)車輛的性能,并及時(shí)地制定出相應(yīng)的調(diào)整策略來(lái)提高車輛的可靠性和安全性。因此,本文從如何更加合理利用傳感器信息的角度出發(fā)對(duì)車輛運(yùn)行狀態(tài)進(jìn)行檢測(cè),并在多傳感器信息融合技術(shù)下對(duì)無(wú)人車行駛策略進(jìn)行了相關(guān)的研究。首先,對(duì)無(wú)人車上基于環(huán)境信息采集系統(tǒng)中各個(gè)傳感器的位置進(jìn)行了設(shè)置；再次,由于環(huán)境信息的復(fù)雜多變,使得傳感器獲得的信息帶有一定程度的不確定性,本文在綜合考慮各類融合算法的基礎(chǔ)上,采取將處理結(jié)果利用D-S證據(jù)理論進(jìn)行融合很好的處理了不確定性的問(wèn)題。最后,針對(duì)D-S證據(jù)理論自身不確定性與不能處理高沖突的問(wèn)題,本文提出矩陣分析的理論修正了該問(wèn)題；通過(guò)上述所得的融合結(jié)果結(jié)合相應(yīng)的準(zhǔn)則,我們就可以分析決策并制定無(wú)人車行駛策略。通過(guò)在實(shí)際道路中進(jìn)行的無(wú)人車行駛實(shí)驗(yàn),得出本文提出的算法能準(zhǔn)確表示駕駛員的不確定性先驗(yàn)知識(shí),能夠保證在一個(gè)或多個(gè)傳感器失效的情況下仍然具有良好的容錯(cuò)性。
[Abstract]:In order to improve road traffic safety, is widely used in various types of unmanned vehicle system of modern vehicle sensors to obtain the necessary environmental information. In the face of the actual environment is gradually complicated, the traditional single sensor because it can not meet the real-time, quickly provide the advantages of high precision and high reliability of monitoring and positioning information, but do not have good autonomy the change of environment, strong adaptability, strong anti-interference and high ratio of performance and price requirements, thus affecting the unmanned vehicle driving strategy. We know that to develop good driving strategy needs to be monitored by a large number of environmental information on the key parts of the vehicle, and to better understand the performance of the whole vehicle, and timely make the corresponding adjustment strategy to improve the reliability and safety of the vehicle. Therefore, this article from how to reasonably use of sensor information. Point of view to detect the running state of the vehicle, and the multi-sensor information fusion technology under the driving strategy of driverless vehicle is studied. Firstly, the unmanned vehicle on each sensor environment information collection system based on the location of the set; thirdly, because the environmental information is complicated and changeable, so that the sensor information obtained with a certain degree of uncertainty, based on the comprehensive consideration of all kinds of fusion algorithms, the results will be taken by the D-S evidence theory in data fusion to deal with uncertain problems. Finally, the needle can not handle the high uncertainty and conflict on D-S evidence theory, this paper puts forward the correction matrix analysis theory through the integration of these problems; the results obtained with the corresponding criterion, we can analyze the decision and to develop unmanned vehicle driving strategy. Through the actual road The experiment of driverless vehicle running in road shows that the algorithm proposed in this paper can accurately express the priori knowledge of uncertainty of drivers, and it can ensure that one or more sensors fail to have good fault tolerance.

【學(xué)位授予單位】：西安工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;U463.6

【引證文獻(xiàn)】

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本文編號(hào)：1475212