An Expeditious and Expressive Vehicle Dynamics Model for Applications in Controls and Reinforcement Learning
Huzaifa Unjhawala,
Thomas Hansen,
Harry Zhang
et al.
Abstract: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++ code, the model runs vehicle dynamics 2000 times faster than real time at a simulation time step of 1 × 10 −3 s on a single core of a commodity CPU. When executed on the GPU, one can perform 300,000 vehicle simulations in real-time. These properties make the model a good candidate for reinforcement learning, model predictive control, model predicti… Show more
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