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

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2023
Language
English
Article Type
Article
Citation

IEEE CONTROL SYSTEMS LETTERS, v.7, pp.1652 - 1657

ISSN
2475-1456
DOI
10.1109/LCSYS.2023.3267041
URI
http://hdl.handle.net/10203/307416
Appears in Collection
EE-Journal Papers(저널논문)
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