Identifying tire and vehicle parameters is an essential step in designing control and planning algorithms for autonomous vehicles. This paper proposes a new method: Simulation-Based Inference (SBI), a modern interpretation of Approximate Bayesian Computation methods (ABC) for parameter identification. The simulation-based inference is an emerging method in the machine learning literature and has proven to yield accurate results for many parameter sets in complex problems. We demonstrate in this paper that it can handle the identification of highly nonlinear vehicle dynamics parameters and gives accurate estimates of the parameters for the governing equations.
翻译:确定轮胎和车辆参数是设计自主车辆控制和规划算法的一个必要步骤,本文件提出了一种新的方法:模拟依据的推论(SBI),对用于参数识别的近巴伊西亚计算法(ABC)的现代解释;模拟依据的推论是机器学习文献中的一种新兴方法,已证明在复杂问题中许多参数组能产生准确的结果;我们在本文件中表明,它可以处理高度非线性车辆动态参数的确定,并准确估计管辖方程的参数。