自动驾驶模糊神经网络速度规划方法  

A velocity planning method based on fuzzy-neural network for autonomous driving

作　　者：王猛[1,2] 陈珏璇 邓正兴 WANG Meng;CHEN Jue-xuan;DENG Zheng-xing(School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074;K+Lab Laboratory,Wuhan KOTEI Informatics Co.,Ltd.,Wuhan 430073;Intelligent Driving Department,Wuhan KOTEI Technology Corporation,Wuhan 430200,China)

机构地区：[1]华中科技大学人工智能与自动化学院,湖北武汉430074 [2]武汉光庭信息技术股份有限公司K+Lab实验室,湖北武汉430073 [3]武汉光庭科技有限公司智能驾驶部,湖北武汉430200

出　　处：《计算机工程与科学》2021年第11期2011-2019,共9页Computer Engineering & Science

基　　金：2019年湖北省博士后创新实践岗位项目。

摘　　要：为提升自动驾驶的舒适性,降低速度规划算法的复杂度,提出了一种基于模糊神经网络的纵向速度规划方法。将人工驾驶经验总结为模糊规则表,建立了模糊速度规划模型,结合神经网络的自学习功能修正模糊速度规划模型,建立了模糊神经网络速度规划模型。分析了静态障碍物和动态障碍物场景,通过仿真验证了所提速度规划方法的可行性,与传统方法相比,加速度的平滑性能更好。所提速度规划方法具有一定的抗干扰性能,工程实现简单,保证了速度规划的实时性与稳定性。In order to improve the comfort performance of autonomous driving and reduce the time complexity of velocity planning algorithm,a longitudinal velocity planning method based on fuzzy neural network is proposed.Manual driving experience is summarized up as a fuzzy rule table,and a fuzzy planning model is established.By utilizing the self-learning function of neural network,the fuzzy planning model is modified,so as to build the fuzzy neural network planning model.Static obstacle scene and dynamic obstacle scene are analyzed.Simulations verify the algorithm feasibility.Compared with the traditional fuzzy planning method,the proposal have smoother acceleration curve.The proposed method has certain anti-disturbance ability,is easy to implement,and ensures the real-time performance and stability.

关 键 词：自动驾驶 速度规划 模糊规划模型 神经网络 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]