  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.
