基于流场的行驶车辆横向安全识别方法研究

发布时间：2018-12-11 04:23

【摘要】：由于道路交通安全保障形势严峻，行驶车辆对适应性强的安全预警系统有着切实的需求。国内外在这方面的研究还处于探索性阶段，就实用性而言，还有继续改进的可能和必要。因此，本文以车辆外流场分布为切入点，综合运用车辆外流场数值模拟，行驶轨迹预测和模糊模式识别三种手段，来共同完成对行驶车辆的横向安全状态的预警。安全预警系统的适应性和准确性得以提高。研究所围绕的主要内容包括：首先，对车辆外流场分布进行CFD模拟计算。对影响流场分布的因素对比分析，选定合适的参数，建立几个典型模型。分别在直线和曲线行驶下，对单车及多车的外流场进行数值模拟。验证线性叠法加获取车辆外流场分布信息的可行性，，并构建数据库。然后，预测行驶车辆的轨迹和获取车辆状态特征。推导车辆的行驶轨迹方程并在MATLAB中求解轨迹曲线。借助道路图像的灰度特征，拟合出车道边界线。根据横向安全状态的各个特征的分析，选出车辆横向位置、撞线时间及流场各点的纵向流速和横向流速等四个状态特征指标。最后，对车辆安全状态模式进行识别。运用模糊动态聚类对特征样本集进行模式类别划分，组建标准的模式类别库。并据此，对行驶车辆的实时状态特征指标所属的安全模式类别进行识别和预警。与传统车道偏离预警相比，本文提出的车辆安全状态识别方法，引入了车辆外流场分布特征，信息指标和适用工况更多，对横向安全状态的判别更精。经过检验，识别效果比较满意。
[Abstract]:Because of the serious situation of road traffic safety guarantee, driving vehicles have practical demand for adaptive safety early warning system. The domestic and foreign research in this field is still in the exploratory stage, in terms of practicability, it is possible and necessary to continue to improve. Therefore, this paper takes the vehicle outflow field distribution as the breakthrough point, synthetically uses the vehicle outflow field numerical simulation, the traveling track forecast and the fuzzy pattern recognition three means, completes the traveling vehicle transverse safety state early warning together. The adaptability and accuracy of the security early warning system have been improved. The main contents of the research are as follows: first, the distribution of vehicle outflow field is simulated by CFD. By comparing and analyzing the factors affecting the distribution of flow field, the appropriate parameters are selected and several typical models are established. The flow field of a bicycle and a multi-vehicle is numerically simulated under straight line and curve respectively. To verify the feasibility of linear stacking method to obtain the distribution information of vehicle outflow field, and to construct the database. Then, the trajectory of the vehicle is predicted and the state characteristics of the vehicle are obtained. The vehicle trajectory equation is derived and the trajectory curve is solved in MATLAB. With the help of the grayscale features of the road image, the lane boundary line is fitted. According to the analysis of the characteristics of the transverse safe state, four state characteristic indexes are selected: the transverse position of the vehicle, the time of the collision line and the longitudinal velocity and the transverse velocity of the points in the flow field. Finally, the vehicle safety state pattern is recognized. Fuzzy dynamic clustering is used to classify the feature sample set, and a standard pattern class database is constructed. Based on this, the classification of safety mode which belongs to the real-time state characteristic index of moving vehicle is identified and early warning is carried out. Compared with the traditional lane deviation warning, the vehicle safety state recognition method proposed in this paper introduces the distribution characteristics of the vehicle outflow field, the information index and the applicable working condition, and the discrimination of the lateral safety state is more accurate. After testing, the recognition effect is satisfactory.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U495;TP391.41

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