Data-Driven Model Identification via Hyperparameter Optimization for Autonomous Racing Systems
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In this letter, we propose a model identification method via hyperparameter optimization (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, the learned parametric models demonstrate good fitness to given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system. The tests show that our race systems leverage the learned model dynamics and successfully perform obstacle avoidance and high-speed driving over 200 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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