本文關(guān)鍵詞：車(chē)路協(xié)作式交叉口車(chē)速引導(dǎo)技術(shù)研究　出處：《北方工業(yè)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 車(chē)路協(xié)同技術(shù) 車(chē)速引導(dǎo) 多智能體理論 智能網(wǎng)聯(lián)汽車(chē) 車(chē)輛編隊(duì)控制

【摘要】：隨著社會(huì)的發(fā)展,機(jī)動(dòng)車(chē)保有量和道路交通量急劇增加,僅靠傳統(tǒng)的交通管理及技術(shù)已無(wú)法解決交通擁堵、交通安全等問(wèn)題。而車(chē)路協(xié)同技術(shù)是智能交通的重要組成部分,該技術(shù)的運(yùn)用將有助于提高城市道路交通安全,緩解擁堵,降低尾氣排放,進(jìn)而能夠有效提高道路交通整體效益;智能網(wǎng)聯(lián)車(chē)輛是指搭載先進(jìn)的車(chē)載傳感器、控制器和執(zhí)行器等裝置,并融合現(xiàn)代通信與網(wǎng)絡(luò)技術(shù),使車(chē)輛具備復(fù)雜環(huán)境感知、智能化決策與控制等功能,同時(shí)可以實(shí)現(xiàn)安全、節(jié)能、環(huán)保及舒適行駛的新一代智能車(chē)輛。結(jié)合智能網(wǎng)聯(lián)車(chē)輛和車(chē)路協(xié)同的特點(diǎn),本文在車(chē)路協(xié)同環(huán)境下首先對(duì)單車(chē)進(jìn)行車(chē)速引導(dǎo)研究,進(jìn)一步結(jié)合多智能體理論對(duì)車(chē)輛編隊(duì)進(jìn)行研究。研究工作總結(jié)如下:第一,交叉口車(chē)輛速度引導(dǎo)研究。本文首先對(duì)車(chē)路協(xié)同系統(tǒng)場(chǎng)景進(jìn)行設(shè)計(jì),然后結(jié)合車(chē)輛運(yùn)動(dòng)規(guī)律及交叉口信號(hào)控制等因素,建立交叉口速度引導(dǎo)模型,進(jìn)一步考慮車(chē)輛在行駛過(guò)程中能與前車(chē)進(jìn)行信息交互,通過(guò)運(yùn)動(dòng)學(xué)規(guī)律計(jì)算車(chē)輛行駛中的實(shí)時(shí)安全距離,并以車(chē)均延誤最小為目標(biāo),建立了速度引導(dǎo)模型。對(duì)車(chē)輛速度引導(dǎo)模型進(jìn)行仿真驗(yàn)證,并分析仿真結(jié)果。第二,車(chē)輛編隊(duì)控制算法研究。傳統(tǒng)車(chē)輛編隊(duì)是通過(guò)車(chē)輛間的跟馳行為形成隊(duì)列的,形成的隊(duì)列不穩(wěn)定且不受控。因此本文首先對(duì)車(chē)輛編隊(duì)進(jìn)行圖論描述,然后運(yùn)用位移和速度等參數(shù)建立二階多智能體方程以加速度作為系統(tǒng)控制輸入,并對(duì)控制輸入進(jìn)行設(shè)計(jì),進(jìn)而建立了適合城市道路車(chē)輛編隊(duì)的控制模型,并進(jìn)行仿真驗(yàn)證。第三,車(chē)輛編隊(duì)與交叉口信號(hào)控制結(jié)合。在以上研究的基礎(chǔ)上,將車(chē)輛編隊(duì)運(yùn)用在交叉口,結(jié)合交叉口信號(hào)控制建立智能網(wǎng)聯(lián)車(chē)輛編隊(duì)控制模型。該模型首先對(duì)交叉口信號(hào)狀態(tài)進(jìn)行劃分,然后對(duì)于進(jìn)入編隊(duì)區(qū)域的車(chē)輛的隊(duì)列規(guī)模進(jìn)行劃分,同時(shí)對(duì)進(jìn)入編隊(duì)區(qū)域的車(chē)輛進(jìn)行編隊(duì),最后隊(duì)列通過(guò)速度引導(dǎo)不停車(chē)通過(guò)交叉口。通過(guò)Vissim/Vb/Matlab聯(lián)合仿真驗(yàn)證模型的有效性。
[Abstract]:With the development of society, the amount of vehicle and traffic volume increase sharply, and the traditional traffic management technology has been unable to solve the traffic congestion, traffic safety and other issues. And the vehicle road coordination technology is an important part of the intelligent transportation system, the use of the technology will help improve the city road traffic safety, alleviate congestion, reduce exhaust emissions, and can effectively improve the overall efficiency of the road traffic network; intelligent vehicle is equipped with advanced vehicle sensor, controller and actuator device, and the integration of modern communication and network technology, the vehicle with complex environment perception, intelligent decision-making and control functions, and can realize the safety, energy saving, environmental protection and comfort driving a new generation of intelligent vehicle. According to the characteristics of intelligent vehicles and road network collaborative environment, first bicycle car speed on the road vehicle guidance research collaboration, in One step based on multi-agent theory to study vehicle formation. Research work is summarized as follows: first, study guide intersection vehicle speed. This paper carries on the design to the CViS scene, and then combined with the vehicle motion and the intersection signal control and other factors, the establishment of the intersection speed guidance model, vehicle information can be further considered the interaction with the front of the car in the running process, real-time calculation of safety distance of vehicles through the kinematics rule, and the average vehicle delay minimum as the goal, set up speed guidance model. The vehicle speed guide model simulation and analysis of simulation results. Second algorithms, the study on formation control of vehicles. The traditional vehicle formation by vehicle the following behavior form a queue, queue formed are not stable and controlled. Therefore this paper first graph description of the vehicle fleet, and then use a Displacement and velocity parameters to establish two order multi-agent system equation for the acceleration as control inputs, and the control input is designed, and then established the control model for city road vehicle fleet, and simulation. Third, vehicle formation combined with intersection signal control. On the basis of the above research, the vehicle fleet in the intersection, the establishment of intelligent control network of vehicle formation control model with the intersection signal. The model first to classify the intersection signal, and then to scale into the region of the vehicle queue formation in the division, at the same time to enter the area of the vehicle fleet formation, finally through the queue speed guidance through the intersection without stopping. The effectiveness of the joint simulation of Vissim/Vb/Matlab model.

【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U491

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