SynChrono: An MPI-Based, Scalable Physics-Based Simulation Framework for Autonomous Vehicles Operating in Off-Road Conditions

Authors: Jay Taves, Aaron Young, Asher Elmquist, Dan Negrut, and Radu Serban (University of Wisconsin) and Simone Benatti and Alessandro Tasora (University of Parma, Italy)

Abstract: In this contribution we outline the MPI-based, scalable, physics-based simulation framework SynChrono, and its use in autonomous vehicle studies in off-road conditions. SynChrono builds on the simulation capabilities of Chrono, but shows better scaling behavior, making it a useful environment for multi-vehicle mobility studies. When combined with GymChrono, a platform based on OpenAI Gym for reinforcement learning, SynChrono serves as a useful tool for integrated vehicle studies in both on- and off-road environments.

Best Poster Finalist (BP): no

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