自动驾驶汽车下匝道路径优化控制策略研究
发布时间：2018-09-01 16:04
【摘要】：随着社会不断进步,经济快速发展,科学技术也在突飞猛进,交通行业是典型的领域之一。现阶段的交通发展以实现智能交通系统为目标,正逐渐从信息化步入智能化,朝着智慧化迈进。近年来,一系列先进的理念和技术用来解决交通拥堵、道路安全、能源消耗和环境污染等问题,自动驾驶汽车就是发展最迅速的代表。现阶段针对无人驾驶汽车的研究多集中在车辆制造技术,而对其在现实场景中的仿真研究,尤其是对高速公路下匝道过程的研究甚少。为此,本文依托国家自然科学基金项目“混入自动驾驶汽车的高速公路交通流微观建模与仿真方法研究”,以手动驾驶汽车下匝道行为研究和已有自动驾驶汽车实地试验研究为基础,建立自动驾驶汽车下匝道的交通流模型；提出自动驾驶汽车下匝道路径优化控制策略,运用计算机仿真手段,确定其最优的下匝道路径控制策略,为面向自动驾驶汽车的高速公路管理与控制提供前瞻性研究基础。首先,在把握自动驾驶汽车研发进展和实地试验的基础之上,对自动驾驶汽车跟车模型以及常规换道模型进行综述,并且从宏观和微观两个层面分析了出口匝道交通流特性,同时根据自动驾驶汽车的环境感知系统确定其检测半径。其次,建立自动驾驶汽车元胞自动机换道模型。本文选取Jiang提出的自适应巡航控制模型作为自动驾驶汽车的跟车规则；在换道规则方面,分自由换道和强制换道。对于非下匝道车辆,引入冒险因子,建立自动驾驶汽车的自由换道模型；面向下匝道车辆的强制换道方面,将到达出口匝道前的强制换道全过程细分为自动采集数据、确定理论安全间距、实测间距优劣排序、实测间距安全分类和换道选择与实行五个步骤,进而完成强制换道模型的构建。再次,以分级分段控制思想为指导,提出自动驾驶汽车下匝道路径控制策略,分为阶梯策略、全域策略和混合策略,建立灵活可变的仿真场景；同时确定策略评价的宏观指标和微观指标,形成最终的综合成本评价模型。其中该模型参数的标定采用专家打分法和层次分析法相结合。最后,开展对自动驾驶汽车下匝道路径优化控制策略的仿真工作,并对其评价分析,主要包括交通流基本参数,平均行程时间以及包括换道时间、间距、换道率等在内的换道行为特性分析。再者对比换道过程的七项成本,分析连续策略的综合成本,最终确定自动驾驶汽车下匝道的最优路径控制策略。
[Abstract]:With the progress of society and the rapid development of economy, science and technology are advancing by leaps and bounds. Transportation industry is one of the typical fields. At the present stage, the traffic development aims at realizing intelligent transportation system, and is gradually moving from informationization to intelligence. In recent years, a series of advanced concepts and technologies have been used to solve the problems of traffic congestion, road safety, energy consumption and environmental pollution. At present, the research on driverless vehicles is mainly focused on the vehicle manufacturing technology, but the simulation research on it in the real scene, especially on the off-ramp process of freeway is very little. Therefore, based on the project of National Natural Science Foundation of China, "study on microscopic modeling and simulation method of freeway traffic flow mixed with self-driving vehicle", Based on the research on the off-ramp behavior of manual driving vehicle and the field test of the existing self-driving vehicle, the traffic flow model of the downramp of the self-driving vehicle is established, and the optimal control strategy for the off-ramp path of the self-driving vehicle is proposed. By means of computer simulation, the optimal off-ramp path control strategy is determined, which provides a prospective research basis for the expressway management and control for self-driving vehicles. First of all, on the basis of grasping the research and development progress and field test of self-driving vehicle, the paper summarizes the model of self-driving vehicle and the conventional change model, and analyzes the traffic flow characteristics of exit ramp from the macro and micro levels. At the same time, the detection radius is determined according to the environment sensing system of the autonomous vehicle. Secondly, the model of automatic driving car cellular automata is established. In this paper, the adaptive cruise control model proposed by Jiang is chosen as the car-following rule for self-driving vehicles, and in the changing rules, it is divided into two parts: free change of track and forced change of course. For the off-ramp vehicle, risk factor is introduced to establish the free change model of the self-driving vehicle, and the whole process of forced lane change before the off-ramp is reached is subdivided into automatic data collection. The five steps of determining the theoretical safety distance, ranking the measured distance, classifying the measured distance and selecting and implementing the change of track are five steps to complete the construction of the forced change model. Thirdly, under the guidance of the idea of hierarchical and piecewise control, this paper puts forward the strategy of off-ramp path control for self-driving vehicles, which can be divided into ladder strategy, global strategy and hybrid strategy, and a flexible and variable simulation scenario is established. At the same time, the macro and micro indexes of strategy evaluation are determined to form the final comprehensive cost evaluation model. The calibration of the model parameters is based on the combination of expert scoring method and Analytic hierarchy process (AHP). Finally, the simulation work of the optimal control strategy for the off-ramp path of the self-driving vehicle is carried out, and its evaluation and analysis are carried out, including the basic parameters of the traffic flow, the average travel time, the changing time and the spacing. Analysis of the characteristics of the changing channel behavior including the rate of change of channel etc. Furthermore, by comparing the seven costs of changing lanes, the comprehensive cost of continuous strategy is analyzed, and the optimal path control strategy of the off-ramp of self-driving vehicles is finally determined.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.6
[Abstract]:With the progress of society and the rapid development of economy, science and technology are advancing by leaps and bounds. Transportation industry is one of the typical fields. At the present stage, the traffic development aims at realizing intelligent transportation system, and is gradually moving from informationization to intelligence. In recent years, a series of advanced concepts and technologies have been used to solve the problems of traffic congestion, road safety, energy consumption and environmental pollution. At present, the research on driverless vehicles is mainly focused on the vehicle manufacturing technology, but the simulation research on it in the real scene, especially on the off-ramp process of freeway is very little. Therefore, based on the project of National Natural Science Foundation of China, "study on microscopic modeling and simulation method of freeway traffic flow mixed with self-driving vehicle", Based on the research on the off-ramp behavior of manual driving vehicle and the field test of the existing self-driving vehicle, the traffic flow model of the downramp of the self-driving vehicle is established, and the optimal control strategy for the off-ramp path of the self-driving vehicle is proposed. By means of computer simulation, the optimal off-ramp path control strategy is determined, which provides a prospective research basis for the expressway management and control for self-driving vehicles. First of all, on the basis of grasping the research and development progress and field test of self-driving vehicle, the paper summarizes the model of self-driving vehicle and the conventional change model, and analyzes the traffic flow characteristics of exit ramp from the macro and micro levels. At the same time, the detection radius is determined according to the environment sensing system of the autonomous vehicle. Secondly, the model of automatic driving car cellular automata is established. In this paper, the adaptive cruise control model proposed by Jiang is chosen as the car-following rule for self-driving vehicles, and in the changing rules, it is divided into two parts: free change of track and forced change of course. For the off-ramp vehicle, risk factor is introduced to establish the free change model of the self-driving vehicle, and the whole process of forced lane change before the off-ramp is reached is subdivided into automatic data collection. The five steps of determining the theoretical safety distance, ranking the measured distance, classifying the measured distance and selecting and implementing the change of track are five steps to complete the construction of the forced change model. Thirdly, under the guidance of the idea of hierarchical and piecewise control, this paper puts forward the strategy of off-ramp path control for self-driving vehicles, which can be divided into ladder strategy, global strategy and hybrid strategy, and a flexible and variable simulation scenario is established. At the same time, the macro and micro indexes of strategy evaluation are determined to form the final comprehensive cost evaluation model. The calibration of the model parameters is based on the combination of expert scoring method and Analytic hierarchy process (AHP). Finally, the simulation work of the optimal control strategy for the off-ramp path of the self-driving vehicle is carried out, and its evaluation and analysis are carried out, including the basic parameters of the traffic flow, the average travel time, the changing time and the spacing. Analysis of the characteristics of the changing channel behavior including the rate of change of channel etc. Furthermore, by comparing the seven costs of changing lanes, the comprehensive cost of continuous strategy is analyzed, and the optimal path control strategy of the off-ramp of self-driving vehicles is finally determined.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.6
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相关期刊论文 前10条
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