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

被引：0
|
作者：
Zhang J. [1 ,2 ]
Wang C. [1 ]
Guo C. [1 ]
Teng F. [1 ]
Li D. [1 ]
机构：
[1] Jilin University, State Key Laboratory of Automotive Simulation and Control, Changchun
[2] Intelligent Network R& D Institute, China FAW Group Co., Ltd., Changchun
来源：
Qiche Gongcheng/Automotive Engineering | 2021年 / 43卷 / 03期
关键词：
Adaptive genetic algorithm; Adaptive neuro-fuzzy inference system; Automated training framework; Parallel parking path planning; Quasi-Newton algorithm;
D O I：
10.19562/j.chinasae.qcgc.2021.03.003
中图分类号：
学科分类号：
摘要：
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. © 2021, Society of Automotive Engineers of China. All right reserved.
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页码：323 / 329
页数：6
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