智能车辆路径规划与转向避障控制方法研究  

Path Planning and Steering Obstacle Avoidance Control Method of Intelligent Vehicle

作　　者：王晓庆 崔同川 孙伟程 李育隆 WANG Xiaoqing;CUI Tongchuan;SUN Weicheng;LI Yulong(School of Automobile,Chang'an University,Xi'an 710064,China)

机构地区：[1]长安大学汽车学院,陕西西安710064

出　　处：《汽车实用技术》2025年第4期27-33,共7页Automobile Applied Technology

摘　　要：基于模型预测控制(MPC)的局部路径规划算法在自动驾驶领域的应用越发广泛,但该方法易受外界参数变动导致规划失效与避障效果不良。针对该问题,提出包含轨迹偏差、前轮偏角及避障功能的目标函数,在满足约束的条件下进行仿真分析,同时建立了包含车速、附着率、避障权值的非线性模型,对局部路径规划与避障功能进行优化。通过设计多项式拟合、自适应模糊逻辑及反向传播(BP)神经网络三种控制器,实现了避障权值的自适应控制。搭建Simulink/CarSim联合仿真平台针对三种控制器进行局部路径规划与避障功能效果验证。结果表明,BP神经网络控制器综合性能最优,对不同路面附着条件、车速和期望路径均有良好的适应性。The application of model predictive control(MPC)based local path planning algorithm in the field of autonomous driving is becoming increasingly widespread,but this method is prone to planning failure and poor obstacle avoidance effect caused by external parameter changes.To address this issue,an objective function is proposed that includes trajectory deviation,front wheel deflection angle,and obstacle avoidance function.Simulation analysis is conducted under the constraint conditions,and a nonlinear model is established that includes vehicle speed,adhesion rate,and obstacle avoidance weight to optimize local path planning and obstacle avoidance function.By designing three controllers,namely polynomial fitting,adaptive fuzzy logic,and back propagation(BP)neural network,adaptive control of obstacle avoidance weights has been achieved.Build a Simulink/CarSim joint simulation platform to verify the effectiveness of local path planning and obstacle avoidance functions for three controllers.The results show that the BP neural network controller has the best comprehensive performance and good adaptability to different road adhesion conditions,vehicle speeds,and expected paths.

关 键 词：智能车辆 模型预测控制 局部路径规划 紧急避障 自适应控制 神经网络 

分 类 号：U463.6[机械工程—车辆工程] U495[交通运输工程—载运工具运用工程]