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In this letter, we propose a model parameter identification method via a
hyperparameter optimization scheme (MIHO). Our method adopts an efficient
explore-exploit strategy to identify the parameters of dynamic models in a
data-driven optimization manner. We utilize MIHO 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, MIHO exhibits more than 13
times faster convergence than traditional parameter identification methods.
Furthermore, the parametric models learned via MIHO 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 MIHO and videos of the tests are available at
https://github.com/hynkis/MIHO.
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