【摘要】：隨著計算機科學(xué)和機器人技術(shù)的發(fā)展,無人駕駛車輛在軍事、民用和科學(xué)研究等諸多方面得到了廣泛的應(yīng)用,它集中了結(jié)構(gòu)學(xué)、電子學(xué)、控制論和人工智能等多學(xué)科的最新研究成果,具有廣闊的應(yīng)用前景。 對于無人駕駛車輛來說,智能決策是其關(guān)鍵組成部分,是研究的熱點之一。在城區(qū)環(huán)境中,由于駕駛場景復(fù)雜多變,交通參與者的行為難以預(yù)測,無法采用一個標準、統(tǒng)一的決策模型進行描述。為了解決城區(qū)復(fù)雜環(huán)境下無人駕駛車輛的決策、規(guī)劃問題,本文通過學(xué)習(xí)人類駕駛員復(fù)雜場景下的決策過程,提出了一種新的駕駛行為決策模型的建立方法,并在此基礎(chǔ)上,完成了無人車的運動規(guī)劃。具體的研究內(nèi)容如下所示： 1)介紹了無人駕駛車輛的研究意義,了解了無人駕駛車輛的國內(nèi)外研究現(xiàn)狀,調(diào)研了移動機器人智能決策及運動規(guī)劃方法,分析比較了國外無人駕駛車輛決策系統(tǒng)的實現(xiàn)方式,對城市典型交通狀況進行了描述和總結(jié)�；隈{駛員的任務(wù)需求以及其對車輛的決策過程及行為方式,提出了無人駕駛車輛在城市道路中進行決策規(guī)劃的關(guān)鍵問題,明確了決策系統(tǒng)的設(shè)計準則。隨后介紹了“智能先鋒Ⅱ”無人駕駛車輛平臺的各個組成模塊,闡述了平臺的工作原理及協(xié)作方式�；跊Q策系統(tǒng)時間和空間上的多分辨率的特點,設(shè)計了三層模塊化決策系統(tǒng)框架,滿足決策系統(tǒng)的實時性、自適應(yīng)性和魯棒性要求。 2)針對不同的駕駛環(huán)境及人類駕駛員的駕駛行為特征,采用層次有限狀態(tài)機的方法建立城區(qū)環(huán)境下無人駕駛車輛行為決策模塊。對駕駛員的復(fù)雜行為進行抽象和分解,并把分解所得的原子行為作為狀態(tài)機的底層狀態(tài)集合。同時,基于人類駕駛員復(fù)雜場景下的決策過程,提出了一種基于多屬性決策方法的駕駛行為決策模型,抽取行駛過程中駕駛員關(guān)注的相關(guān)屬性,判斷和評價并獲取最終駕駛行為模式,使得行為決策模式符合人類駕駛員的思維過程,解決了城市復(fù)雜交通場景下無人駕駛車輛的類人決策問題。設(shè)計了一種基于層次分析法——熵權(quán)法的駕駛行為矩陣賦權(quán)方法,建立基于駕駛經(jīng)驗與客觀數(shù)據(jù)的權(quán)重體系,減弱主觀隨意性對決策結(jié)果的干擾,并且減少因樣本數(shù)據(jù)不足帶來的熵值法不準確的問題。結(jié)合TOPSIS優(yōu)選和灰色關(guān)聯(lián)分析兩種方法,構(gòu)建一種新的駕駛行為灰色理想值逼近模型,進行決策評判,使得被選方案不僅在空間位置上與最優(yōu)方案較為接近,同時,其形狀也貼近于最優(yōu)方案,保證了所選駕駛行為的最優(yōu)性。 3)研究基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的運動規(guī)劃方法。首先對城區(qū)環(huán)境中運動規(guī)劃算法的設(shè)計原則進行了分析,明確了運動規(guī)劃的難點。針對非結(jié)構(gòu)化道路特征不明顯,環(huán)境不可預(yù)測的特點,提出了一種基于徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)的運動規(guī)劃方法,通過隨機提取可行駛區(qū)域內(nèi)的離散參考點,采用正則化網(wǎng)絡(luò)對數(shù)據(jù)做逼近處理,并以一種帶遺忘因子的單輸出RBF網(wǎng)絡(luò)學(xué)習(xí)方法——梯度下降法對網(wǎng)絡(luò)參數(shù)進行學(xué)習(xí)。生成的軌跡能夠?qū)θ我獾缆沸螤钸M行擬合,并且滿足車輛運動特性的約束。此外由于RBF網(wǎng)絡(luò)是一種局部逼近網(wǎng)絡(luò),具有學(xué)習(xí)速度快的優(yōu)點,對于動態(tài)變化的環(huán)境能夠快速響應(yīng),滿足車輛規(guī)劃系統(tǒng)的實時性要求。 最后,本文基于城區(qū)真實道路環(huán)境,在“智能先鋒Ⅱ”無人駕駛車輛平臺上進行了實驗,結(jié)果驗證了決策系統(tǒng)設(shè)計方法的正確性和有效性。
[Abstract]:With the development of computer science and robotics, unmanned vehicles have been widely used in many fields, such as military, civil and scientific research. It focuses on the latest research achievements in many disciplines, such as structure, electronics, cybernetics and artificial intelligence, and has a broad application prospect.
Intelligent decision making is one of the key components of unmanned vehicle, which is one of the key parts of research. In urban environment, because of the complex and changeable driving scene, the behavior of the traffic participants is difficult to predict. It is impossible to use a standard and unified decision model to describe the unmanned vehicle in the complex environment of urban area. On the basis of learning the decision-making process under the complex scene of human drivers, a new method of driving behavior decision model is proposed. On this basis, the motion planning of unmanned vehicle is completed. The specific content of the research is as follows:
1) introduce the research significance of unmanned vehicle, understand the research status of unmanned vehicle at home and abroad, investigate the intelligent decision making and motion planning method of mobile robot, analyze and compare the realization way of the decision system of unmanned vehicle in foreign countries, describe and summarize the typical traffic situation of the city, and based on the driver's task. Demand and its decision-making process and behavior of vehicle, the key problem of decision planning of unmanned vehicle in urban road is put forward, and the design criterion of decision system is clarified. Then, each component module of "intelligent pioneer II" unmanned vehicle platform is introduced, and the working principle and cooperation mode of the platform are expounded. Based on the multi-resolution characteristics of time and space in the decision system, a framework of three layers of modular decision making system is designed to meet the real-time, adaptive and robust requirements of the decision system.
2) in view of the different driving environment and the driving behavior characteristics of human drivers, a hierarchical finite state machine is used to establish the behavior decision module of unmanned vehicle in the urban environment. The complex behavior of the driver is abstracted and decomposed, and the decomposition of the atomic behavior is used as the bottom state set of the state machine. In the decision-making process of complex drivers, a driving behavior decision model based on multi attribute decision-making method is proposed, which extracts the related attributes concerned by the driver during the driving process, judges and evaluates and obtains the final driving behavior pattern, making the behavior decision model conforms to the thinking process of the man type driver and solves the complex intersection of the city. Based on the entropy weight method, a driving behavior matrix weighting method based on entropy weight method is designed, which is based on the entropy weight method, and establishes a weight system based on the driving experience and objective data to reduce the interference of subjective randomness to the decision results, and to reduce the inaccuracy of the entropy method caused by the lack of sample data. In combination with two methods of TOPSIS optimization and grey relational analysis, a new grey ideal value approximation model of driving behavior is constructed to make decision evaluation, which makes the selected scheme not only close to the optimal scheme in space position, but also its shape is close to the optimal scheme, which ensures the optimal driving behavior.
3) the motion planning method based on radial basis function neural network is studied. First, the design principle of motion planning algorithm in urban environment is analyzed, and the difficulty of motion planning is clarified. A motion based on radial basis function (RBF) neural network is proposed for the characteristics of unstructured road features and unpredictable environment. In the planning method, the discrete reference points in the driving region are randomly extracted, the regularization network is used to approach the data, and the gradient descent method is used to study the network parameters with a single output RBF network learning method with a forgetting factor. The generated trajectory can fit the shape of any road and satisfy the vehicle transportation. In addition, because the RBF network is a local approximation network, it has the advantage of fast learning and fast response to the dynamic changing environment and meets the real-time requirements of the vehicle planning system.
Finally, based on the real road environment of the city, the experiment is carried out on the "intelligent pioneer II" unmanned vehicle platform. The results verify the correctness and effectiveness of the design method of the decision system.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：U463.6;U495

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