基于自适应神经模糊推理系统的平行泊车路径规划

Vehicle Parallel Parking Path Planning Based on Adaptive Neuro-fuzzy Inference System

针对常见的平行泊车场景,提出一种基于自适应神经模糊推理系统的平行泊车路径规划方法。以基于优化算法的泊车路径规划方法得出的泊车路径作为训练样本,利用Python脚本语言建立以自适应遗传算法和拟牛顿法为内核的自动化训练框架,使自动训练后的自适应神经模糊推理系统既可继承基于优化算法的泊车路径规划方法适用范围更广的优势,又有效解决该方法求解过程计算量大的问题。通过仿真分析验证所提出方法的可行性和有效性,结果表明:自动训练后的自适应神经模糊推理系统可依据汽车初始泊车位姿和泊车位信息快速规划出可行的平行泊车路径。

Aiming at the common parallel parking scene,a novel parallel parking path planning method for vehicle is proposed based on adaptive neuro-fuzzy inference system.With the parking path obtained from parallel parking path planning based on optimization algorithm as training sample,Python script is used to build the automated training framework with adaptive genetic algorithm and quasi-Newton algorithm as its core for enabling the adaptive neuro-fuzzy inference system automatically trained can not only inherit the merits of wider application scope of parallel parking path planning method based on optimization algorithm,but also effectively get rid of enormous computation efforts.The feasibility and effectiveness of the proposed method are verified by simulation,and the results show that the adaptive neuro-fuzzy inference system automatically trained can quickly plan the feasible parallel parking path based on the initial parking posture of vehicle and parking space information.