An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning

An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning

We present a Vehicle Model (VM) that has 17 degrees of freedom and includes nonlinear tire and powertrain subsystems. Implemented as a relatively small piece of C&#x002B;&#x002B; code, the model runs vehicle dynamics 2000 times faster than real time at a simulation time step of <inline-fo...

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Bibliographic Details
Main Authors: Huzaifa Unjhawala, Thomas Hansen, Harry Zhang, Stefan Caldraru, Shouvik Chatterjee, Luning Bakke, Jinlong Wu, Radu Serban, Dan Negrut
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Vehicle models
Bayesian inference
calibration
state estimation
control
machine learning
Online Access:https://ieeexplore.ieee.org/document/10443432/

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https://ieeexplore.ieee.org/document/10443432/

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