自动驾驶车辆行进参数有限时间估计方法研究

Research on the Finite Time Estimation Method of Driving Parameters of Autonomous Vehicles

依靠车辆自动驾驶系统快速与高效的环境感知能力,可有效减少人为操控失误、降低交通事故发生率。针对自动驾驶车辆在车体和路况信息等参数估计过程中存在的时间延迟、误差较大的问题,提出一种考虑有限时间的自动驾驶车辆行进参数估计方法。首先通过构建辨识参数的仿射参数模型,推导了参数估计误差向量,并基于有限时间收敛理论设计了自适应参数更新律。同时,采用并行学习技术,对参数收敛过程的持续激励条件进行优化,从而简化估计过程,以期降低系统运行成本和响应时间。最后,利用所设计的车辆参数估计方法对车辆参数、路面状况等指标进行仿真验证。仿真结果表明,较之现有方法,所提方法能够实现快速地参数收敛,且收敛结果稳定、准确可靠。

Relying on the fast and efficient environmental perception ability of vehicle automatic driving system can effectively reduce human error and reduce the incidence of traffic accidents.Aiming at the problem of time delay and error in the process of vehicle body and road condition information estimation,a limited time estimation method is proposed.Firstly,the parameter estimation error vector is derived by constructing the affine parameter model of identification parameters,and the adaptive parameter updating law is designed based on the finite time convergence theory.At the same time,parallel learning technology is used to optimize the continuous excitation conditions of the parameter convergence process,so as to simplify the estimation process and reduce the operating cost and response time of the system.Finally,the vehicle parameters and road conditions are simulated and verified by the designed vehicle parameter estimation method.The simulation results show that compared with the existing methods,the proposed method can achieve fast parameter convergence,and the convergence results are stable,accurate and reliable.