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High-Fidelity Multi-Body Simulator for Autonomous Racing

arXiv2 min read

arXiv:2609.12795v1 Announce Type: new Abstract: We present a custom high-fidelity vehicle dynamics simulation environment for testing and validation of Autonomous Racing software. The digital twin of the autonomous vehicle is developed in Dymola, using racecar dynamics modeling libraries to build a complete multi-body model. A 3D road surface, including elevation profiles and curbs, is implemented using the Curved Regular Grid (CRG) standard. The model is exported from Dymola as a Functional Mock-up Unit (FMU) and integrated into a custom software-in-the-loop simulator, where communication interfaces with the autonomous racing stack were developed in C++. A calibration procedure based on experimental data is also presented, along with a validation study to further support the quality of the proposed framework. The simulator runs in real time on a portable computer and provides reliable ground truth for algorithms validation prior to real-world deployment.

Abstract:We present a custom high-fidelity vehicle dynamics simulation environment for testing and validation of Autonomous Racing software. The digital twin of the autonomous vehicle is developed in Dymola, using racecar dynamics modeling libraries to build a complete multi-body model. A 3D road surface, including elevation profiles and curbs, is implemented using the Curved Regular Grid (CRG) standard. The model is exported from Dymola as a Functional Mock-up Unit (FMU) and integrated into a custom software-in-the-loop simulator, where communication interfaces with the autonomous racing stack were developed in C++. A calibration procedure based on experimental data is also presented, along with a validation study to further support the quality of the proposed framework. The simulator runs in real time on a portable computer and provides reliable ground truth for algorithms validation prior to real-world deployment.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.12795 [cs.RO]

(or arXiv:2609.12795v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.12795 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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