本文選題：自主泊車 + 時空分割�。� 參考：《浙江大學》2016年碩士論文

【摘要】：和人類駕駛員相比,無人車能夠更加全面的掌握即時路況并及時對汽車巡航狀態(tài)進行調(diào)整,從而改善交通擁堵狀況、避免了人為失誤造成的交通事故和傷亡,因此近些年來無人駕駛技術發(fā)展迅速。許多半自動駕駛技術在汽車上已經(jīng)得到了大規(guī)模的普及,比如緊急制動,定速巡航和車道保持等。然而,在行車環(huán)境建模、避障軌跡優(yōu)化等方面還需要深入的研究。如車載傳感器精度有限的情況下如何對障礙環(huán)境建模,如何處理環(huán)境中意外出現(xiàn)的動態(tài)障礙物,如何針對不同的泊車位實現(xiàn)標準化的軌跡優(yōu)化算法設計,如何優(yōu)化智能無信號燈路口下的多車避障軌跡等都是值得研究的問題。本文用動態(tài)優(yōu)化全聯(lián)立算法對上述問題做了一些研究。主要內(nèi)容和成果如下：1.對城市環(huán)境下的自主泊車問題,采用MPCC和R函數(shù)方法對車位環(huán)境建模,與車輛運動學模型、物理約束共同構成了行車系統(tǒng)模型,構造了聯(lián)立框架下的自主泊車動態(tài)優(yōu)化命題。采用有限元正交配置法將原命題離散化為非線性數(shù)學規(guī)劃問題,由非線性求解器高效求解得到具有時間信息的可直接用于指導車輛跟蹤的泊車軌跡。2.針對自主泊車軌跡動態(tài)優(yōu)化命題含有較多復雜約束可能引起的求解困難,提出了時空分割策略來增強優(yōu)化算法的收斂性。通過在軌跡優(yōu)化命題中引入吸引區(qū)、塌縮區(qū)來分割泊車空間,將非線性的復雜環(huán)境約束在割裂空間下進行簡化,重構泊車軌跡優(yōu)化命題。仿真實驗證明了時空分割策略的有效性。3.在城市環(huán)境下基于信息完整假設進行多車軌跡優(yōu)化的全局規(guī)劃算法研究。在多車模型、環(huán)境模型下融合了車-車、車與動態(tài)可預測障礙物的復雜避障約束,構造多車協(xié)作避讓軌跡優(yōu)化命題。數(shù)值實驗表明了基于全聯(lián)立的全局規(guī)劃算法的有效性。4.對于環(huán)境感知不完整的車輛軌跡規(guī)劃問題,基于障礙環(huán)境預測模型進行局部滾動優(yōu)化。運用假設靜態(tài)法、速度切線預測法、完整預測法對障礙環(huán)境建模,根據(jù)障礙車輛進出我車的沖突檢測域來切換重構行車系統(tǒng)軌跡優(yōu)化命題,并比較了預測模型對車輛避障性能的影響。
[Abstract]:Compared with human drivers, the UAV can master the real-time traffic conditions more comprehensively and adjust the vehicle cruising state in time, thus improving the traffic congestion and avoiding the traffic accidents and casualties caused by human error. As a result, driverless technology has developed rapidly in recent years. Many semi-autonomous driving techniques have been widely used in automobiles, such as emergency braking, constant speed cruising and lane maintenance. However, further research is needed in traffic environment modeling and obstacle avoidance trajectory optimization. For example, how to model the obstacle environment, how to deal with the unexpected dynamic obstacles, how to design the standardized trajectory optimization algorithm for different parking spaces, how to model the obstacle environment under the condition of limited precision of the vehicle sensor, how to deal with the unexpected dynamic obstacles in the environment, It is worth studying how to optimize the trajectory of multi-vehicle obstacle avoidance at the intersection of intelligent signal-free. In this paper, the dynamic optimization algorithm is used to study the above problems. The main contents and results are as follows: 1. For the problem of autonomous parking in urban environment, the vehicle parking environment is modeled by MPCC and R function method, and the vehicle kinematics model and physical constraints are combined to form the vehicle system model, and the dynamic optimization proposition of autonomous parking under the simultaneous frame is constructed. The finite element orthogonal collocation method is used to discretize the original proposition into a nonlinear mathematical programming problem. The nonlinear solver is used to efficiently solve the parking trajectory with time information which can be directly used to guide the vehicle tracking. In view of the difficulty of solving the dynamic optimization proposition of autonomous parking trajectory with more complex constraints, a spatio-temporal segmentation strategy is proposed to enhance the convergence of the optimization algorithm. By introducing attraction region and collapsing area into the trajectory optimization proposition, the parking space is separated, and the nonlinear complex environment constraint is simplified in the split space, and the parking trajectory optimization proposition is reconstructed. The simulation results show that the spatio-temporal segmentation strategy is effective. The global planning algorithm for multi-vehicle trajectory optimization based on the assumption of information integrity in urban environment is studied. Under the multi-vehicle model and environment model, the complex obstacle avoidance constraints of vehicle-vehicle, vehicle-vehicle and dynamic predictable obstacles are combined, and the proposition of multi-vehicle cooperative avoidance trajectory optimization is constructed. Numerical experiments show the effectiveness of the global programming algorithm based on full synchronization. 4. 4. For the vehicle trajectory planning problem with incomplete environmental perception, the local rolling optimization based on the obstacle environment prediction model is carried out. Using the hypothesis static method, the velocity tangent prediction method, the complete forecast method to model the obstacle environment, according to the obstacle vehicle entering and leaving our vehicle conflict detection domain to switch the reconstruction train system trajectory optimization proposition, The effect of prediction model on vehicle obstacle avoidance performance is compared.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：U463.6

【參考文獻】

相關期刊論文 前7條

1 韓鵬;單家元;;基于Radau偽譜法的重復使用運載器再入軌跡優(yōu)化(英文)[J];控制理論與應用;2013年08期

2 林蓁蓁;李慶;梁艷菊;陳大鵬;;一種基于路徑規(guī)劃的自動平行泊車算法[J];計算機應用研究;2012年05期

3 郭偉;楊明;王冰;王春香;;基于博弈論的路口多車協(xié)作算法[J];華中科技大學學報(自然科學版);2011年S2期

4 肖永劍;楊建國;王兆安;;基于占先度的駕駛員沖突避碰決策模型[J];交通運輸工程學報;2009年05期

5 郭孔輝;姜輝;張建偉;丁海濤;;基于模糊邏輯的自動平行泊車轉(zhuǎn)向控制器[J];吉林大學學報(工學版);2009年S2期

6 吳堅,王小椿,姜虹,鄭康平,李潤方;多邊形的隱函數(shù)表示法[J];計算機工程與應用;2003年32期

7 劉金義,張紅玲;R-函數(shù)理論介紹及其應用評述[J];工程圖學學報;2001年02期

相關博士學位論文 前7條

1 朱株;基于三維數(shù)據(jù)面向無人車導航的非結(jié)構化場景理解[D];浙江大學;2014年

2 孫勇;基于改進Gauss偽譜法的高超聲速飛行器軌跡優(yōu)化與制導[D];哈爾濱工業(yè)大學;2012年

3 陳楊;基于微分—代數(shù)混合方程機理模型的非線性預測控制[D];浙江大學;2011年

4 王志強;微分代數(shù)方程動態(tài)優(yōu)化問題的快速求解策略研究[D];浙江大學;2012年

5 陳偉鋒;大規(guī)模復雜過程系統(tǒng)的高性能優(yōu)化理論與方法研究[D];浙江大學;2011年

6 姜輝;自動平行泊車系統(tǒng)轉(zhuǎn)向控制策略的研究[D];吉林大學;2010年

7 王可心;大規(guī)模過程系統(tǒng)非線性優(yōu)化的簡約空間理論與算法研究[D];浙江大學;2008年

相關碩士學位論文 前6條

1 顏豐琳;自由飛行模式的多飛行器軌跡優(yōu)化[D];浙江大學;2015年

2 李勇;無信號燈十字交叉口協(xié)作車輛控制研究[D];北京理工大學;2015年

3 黃森;動態(tài)優(yōu)化問題的移動有限元算法研究[D];浙江大學;2012年

4 高航;自動垂直泊車方法研究[D];中國科學技術大學;2011年

5 孟繁微;車輛垂直泊車轉(zhuǎn)向控制算法研究[D];吉林大學;2011年

6 王芳成;自動平行泊車系統(tǒng)的研究[D];中國科學技術大學;2010年

，

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