摘要
针对自动驾驶汽车在行驶过程中会遇到随时间和交通环境变化的不确定性,须对自动驾驶系统参数进行辨识和学习。通过获取闭环状态下的系统行为和学习数据驱动下的系统参数,可极大提升系统辨识可靠性和稳定性。本文对自动驾驶车辆跟车的典型场景,综合考虑车辆控制和动力学特性,构建了基于车辆运行数据和置信椭圆的系统参数辨识的学习算法,提升系统辨识的可靠性和鲁棒性。结果表明提出的参数辨识和学习方法可准确估计车辆参数。
System parameters of autonomous vehicles need to be identified and learned during operation to solve the problem that autonomous vehicles encounter many uncertainties that change with time, operating conditions, and environments. By capturing system behavior in a closed-loop setting and using data to learn the related parameters, system reliability and robustness can be quantitatively established. This paper focuses on a basic scenario of an autonomous vehicle following its front vehicle. By integrating control actions with vehicle dynamics, a learning algorithm using operational data and confidence ellipsoids was employed to support robustness and reliability. A simulation case study was used to illustrate the strategies. The results show the proposed method can estimate the vehicle＇s parameters accurately.
作者
王乐一
殷刚
赵广亮
李升波
徐彪
李克强
WANG Leyi;George Yin;ZHAO Guangliang;LI Shengbo;Xu Biao;LI Keqiang(Department of Electrical and Computer Engineering,Wayne State University,Detroit,MI 48202,USA;Department of Mathematics,Wayne State University,Detroit,MI 48202,USA;GE Global Research Niskayuna,NY 12309,USA;Department of Automotive Engineering Tsinghua University,Beijing 100084,China)
出处
《汽车安全与节能学报》
CAS
CSCD
2018年第2期141-148,共8页
Journal of Automotive Safety and Energy
基金
The National Natural Science Fund Projeet(51575293,51622504)
National Key R&D Program of China(2016YFB0100906)
International Sci&Tech Cooperation Program of China(2016YFE0102200)~~
关键词
汽车控制
自动驾驶车辆
参数辩识
学习
鲁棒性
vehicle control
autonomous vehicle
identification of parameters
learning
robustness
