  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.
