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Article overview
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Data-Driven Model Identification via Hyperparameter Optimization for the Autonomous Racing System | Hyunki Seong
; Chanyoung Chung
; David Hyunchul Shim
; | Date: |
4 Jan 2023 | Abstract: | In this letter, we propose a model identification method via hyperparameter
optimization (MIHO). Our method is able to identify the parameters of the
parametric models in a data-driven manner. We utilize MIHO for the dynamics
parameters of the AV-21, the full-scaled autonomous race vehicle, and integrate
them into our model-based planning and control systems. In experiments, the
models with the optimized parameters demonstrate the generalization ability of
the vehicle dynamics model. We further conduct extensive field tests to
validate our model-based system. The tests show that our race systems leverage
the learned model dynamics well 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 this https URL. | Source: | arXiv, 2301.01470 | Services: | Forum | Review | PDF | Favorites |
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