无人车弯道控制技术研究
发布时间：2019-01-27 11:30
【摘要】：无人驾驶技术成为近年来国内外研究的热点,无人车的弯道控制技术是无人驾驶技术的重要研究方向之一,它主要是将对外界路况的感知、智能与最优控制、路径规划等技术集于一体的综合技术。弯道控制的原理是先通过车载传感器获得无人车自身的状态信息,然后通过物理学关系和预测算法估计得到下一时刻的弯道半径值,根据当前的车速与方向盘转角值,最终使车辆在安全稳定的条件下实现快速的过弯。本文根据无人驾驶智能车在不同弯道工况下实现平稳,快速过弯的功能要求,设计得到道路环境感知,车辆转向行为控制的系统方案。全文围绕无人驾驶智能车的快速平稳的任务要求而展开,进行的工作和研究内容重点包括：根据现实条件和系统基本的要求设计和搭建无人驾驶智能车控制系统,主要包括车载传感器和执行行机构硬件,以上都可用来进行无人车的弯道控制的研究分析。影响车辆转弯的两个主要因素分别是悬架和轮胎。通过建立的线性二自由度车辆模型,研究分析悬架的侧倾特性和轮胎的侧偏特性对弯道控制的影响,之后建立起弯道半径和横摆角速度的运算关系,弯道半径和车厢侧倾角的运算关系。采用卡尔曼滤波算法对横摆角速度预测,自回归分析算法对车身侧倾角的预测实现对弯道半径的预测,最后对无人车的控制调节是根据预测得到的弯道半径值来进行。对于卡尔曼滤波算法进行仿真实验,自回归分析算法采用实际道路环境下的过弯实验,通过对所测数据的分析和讨论,验证两种算法的可行性,对比两种算法在实际控制过程中的优缺点。
[Abstract]:In recent years, driverless technology has become a hot research topic both at home and abroad. The bend control technology of unmanned vehicle is one of the important research directions of driverless technology. It is mainly concerned with the perception, intelligence and optimal control of road conditions. Integrated technology such as path planning. The principle of bend control is to obtain the state information of the unmanned vehicle through the vehicle sensor first, and then to estimate the radius of the bend at the next moment by the physical relation and the prediction algorithm, according to the current speed and steering wheel rotation angle. Finally, the vehicle can quickly overbend under the condition of safety and stability. According to the functional requirements of the driverless intelligent vehicle to achieve smooth and fast overbending under different bend conditions, the system scheme of road environment perception and vehicle steering behavior control is designed in this paper. The full text revolves around the fast and steady task requirement of the unmanned intelligent vehicle. The work and research focus include: design and build the control system of the unmanned intelligent vehicle according to the actual conditions and the basic requirements of the system. The above can be used to study and analyze the bend control of unmanned vehicle. The two main factors that affect the turning of the vehicle are suspension and tire. Based on the linear two-degree-of-freedom vehicle model, the effects of the roll characteristics of suspension and tire sideways on the bend control are studied and analyzed, and the calculation relationship between the curve radius and the yaw angular velocity is established. The relation between the radius of the bend and the roll angle of the car. The Kalman filtering algorithm is used to predict the yaw velocity, the auto-regression algorithm to predict the car body roll angle to predict the curve radius. Finally, the control adjustment of the unmanned vehicle is carried out according to the predicted curve radius value. For the Kalman filter algorithm simulation experiment, the self-regression analysis algorithm uses the actual road environment, through the analysis and discussion of the measured data, the feasibility of the two algorithms is verified. The advantages and disadvantages of the two algorithms in the actual control process are compared.
【学位授予单位】：西安工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.6
本文编号：2416218
[Abstract]:In recent years, driverless technology has become a hot research topic both at home and abroad. The bend control technology of unmanned vehicle is one of the important research directions of driverless technology. It is mainly concerned with the perception, intelligence and optimal control of road conditions. Integrated technology such as path planning. The principle of bend control is to obtain the state information of the unmanned vehicle through the vehicle sensor first, and then to estimate the radius of the bend at the next moment by the physical relation and the prediction algorithm, according to the current speed and steering wheel rotation angle. Finally, the vehicle can quickly overbend under the condition of safety and stability. According to the functional requirements of the driverless intelligent vehicle to achieve smooth and fast overbending under different bend conditions, the system scheme of road environment perception and vehicle steering behavior control is designed in this paper. The full text revolves around the fast and steady task requirement of the unmanned intelligent vehicle. The work and research focus include: design and build the control system of the unmanned intelligent vehicle according to the actual conditions and the basic requirements of the system. The above can be used to study and analyze the bend control of unmanned vehicle. The two main factors that affect the turning of the vehicle are suspension and tire. Based on the linear two-degree-of-freedom vehicle model, the effects of the roll characteristics of suspension and tire sideways on the bend control are studied and analyzed, and the calculation relationship between the curve radius and the yaw angular velocity is established. The relation between the radius of the bend and the roll angle of the car. The Kalman filtering algorithm is used to predict the yaw velocity, the auto-regression algorithm to predict the car body roll angle to predict the curve radius. Finally, the control adjustment of the unmanned vehicle is carried out according to the predicted curve radius value. For the Kalman filter algorithm simulation experiment, the self-regression analysis algorithm uses the actual road environment, through the analysis and discussion of the measured data, the feasibility of the two algorithms is verified. The advantages and disadvantages of the two algorithms in the actual control process are compared.
【学位授予单位】：西安工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.6
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