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arXiv · 2301.01470

Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems

Hyunki Seong·Chanyoung Chung·David Hyunchul Shim
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Abstract

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

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Hyunki Seong, Chanyoung Chung, David Hyunchul Shim. 2023-01-04. Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems. https://doi.org/10.1109/lcsys.2023.3267041

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