DeepAI AI Chat
Log In Sign Up

Identification of Vehicle Dynamics Parameters Using Simulation-based Inference

by   Ali Boyali, et al.

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.


Identification of the nonlinear steering dynamics of an autonomous vehicle

Automated driving applications require accurate vehicle specific models ...

Terrain parameter estimation from proprioceptive sensing of the suspension dynamics in offroad vehicles

Offroad vehicle movement has to contend with uneven and uncertain terrai...

A Note on Simulation-Based Inference by Matching Random Features

We can, and should, do statistical inference on simulation models by adj...

BayesRace: Learning to race autonomously using prior experience

Learning to race autonomously is a challenging problem. It requires perc...

Behavior Identification and Prediction for a Probabilistic Risk Framework

Operation in a real world traffic requires autonomous vehicles to be abl...

Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics

The paper contributes towards the modeling, identification, and control ...