【摘要】：自動駕駛車輛近些年來逐漸成為熱點(diǎn)話題,許多高校、傳統(tǒng)車企和互聯(lián)網(wǎng)企業(yè)紛紛展開研究,且已經(jīng)發(fā)展到一定水平,但若想真正的實(shí)現(xiàn)在真實(shí)城區(qū)道路上行駛,仍有許多問題需要解決。為使自動駕駛車輛能夠在城區(qū)道路環(huán)境中自如行駛,本文針對自動駕駛車輛在城市交通環(huán)境中的行為決策問題展開了深入的研究。針對城區(qū)道路環(huán)境中換道行為,提出了基于駕駛員換道經(jīng)驗(yàn)的自動駕駛車輛換道決策模型。為模仿駕駛員決策過程,提出了類人的自動駕駛車輛的直覺決策方法。首先基于離線學(xué)習(xí),使自動駕駛車輛具有人類駕駛員的駕駛經(jīng)驗(yàn),再利用在線學(xué)習(xí),使自動駕駛車輛在線學(xué)習(xí)駕駛員經(jīng)驗(yàn),從而模擬人類駕駛員行駛過程中經(jīng)驗(yàn)積累的過程。然而由于時(shí)間有限,本文僅針對直覺決策模型中離線學(xué)習(xí)部分展開了深入的研究。本文針對換道場景,提出了基于駕駛員經(jīng)驗(yàn)的自動駕駛車輛換道決策模型,基于粗糙集神經(jīng)網(wǎng)絡(luò)融合算法提取駕駛員換道規(guī)則,在使用粗糙集對駕駛員換道數(shù)據(jù)進(jìn)行規(guī)則提取的過程中,使用人工神經(jīng)網(wǎng)絡(luò)算法保證規(guī)則提取結(jié)果的一致性。規(guī)則提取完成后,使用分層狀態(tài)機(jī)方法建立分層換道規(guī)則庫,將駕駛員規(guī)則應(yīng)用于自動駕駛決策模型中,運(yùn)用Prescan和Simulink/Stateflow實(shí)現(xiàn)城區(qū)道路環(huán)境換道聯(lián)合仿真,仿真結(jié)果表明,該方法可以使自動駕駛車輛在車流中進(jìn)行安全的換道,驗(yàn)證了規(guī)則的有效性。同時(shí),為了驗(yàn)證自動駕駛車輛換道決策模型在真實(shí)城區(qū)道路環(huán)境中的可行性,首先使用V-rep和Visual Studio進(jìn)行聯(lián)合仿真來驗(yàn)證換道決策算法的安全性,之后基于北京理工大學(xué)智能車輛研究所比亞迪自動駕駛車輛在北京市三環(huán)道路上進(jìn)行測試。實(shí)驗(yàn)結(jié)果說明通過本文所建立的決策模型,自動駕駛車輛可以在城區(qū)道路環(huán)境中安全換道。最后對自動駕駛車輛換道決策模型的類人性進(jìn)行了分析,分析結(jié)果表明本文所建立的自動駕駛車輛換道決策模型與人類駕駛員決策較相似,離線學(xué)習(xí)駕駛員經(jīng)驗(yàn)的效果較好。
[Abstract]:In recent years, autonomous vehicles have gradually become a hot topic. Many universities, traditional car companies and Internet enterprises have carried out research, and have developed to a certain level, but if they really want to drive on the roads of real urban areas, There are still many problems to be solved. In order to enable autonomous vehicles to travel freely in urban road environment, this paper focuses on the behavior decision of autonomous vehicles in urban traffic environment. According to the changing behavior in urban road environment, a decision model of automatic driving vehicle change based on driver's experience is proposed. In order to imitate the decision-making process of drivers, a human-like intuitionistic decision-making method for autonomous vehicles is proposed. Firstly, based on off-line learning, the self-driving vehicle has the driving experience of the human driver. Then, the self-driving vehicle can learn the driver's experience online by using the on-line learning, so as to simulate the process of the experience accumulation in the driving process of the human driver. However, due to the limited time, this paper focuses on the offline learning part of the intuitionistic decision model. In this paper, based on driver's experience, a decision model of automatic driving vehicle change is proposed, and the driver changing rules are extracted based on rough set neural network fusion algorithm. In the process of using rough set to extract the rules of the driver's change data, the artificial neural network algorithm is used to ensure the consistency of the rule extraction results. After the rule extraction is completed, the hierarchical change rules database is established by using the hierarchical state machine method, and the driver rules are applied to the automatic driving decision model. The combined simulation of road environment change in urban area is realized by using Prescan and Simulink/Stateflow. The simulation results show that. This method can make the automatic driving vehicle change the lane safely in the traffic flow, and verify the validity of the rules. At the same time, in order to verify the feasibility of the automatic driving vehicle change decision model in the real urban road environment, V-rep and Visual Studio are used to simulate the security of the algorithm. BYD self-driving vehicles based on Beijing Institute of Technology Intelligent vehicle Research Institute were then tested on the third Ring Road in Beijing. The experimental results show that the self-driving vehicle can change lanes safely in the urban road environment through the decision model established in this paper. Finally, the human nature of the automatic driving vehicle change decision model is analyzed. The results show that the model is similar to the human driver's decision, and the effect of off-line learning driver's experience is better.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U463.6

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