Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems
This work addresses the problem of efficient and accurate model parameter identification for autonomous racing systems, representing an incremental improvement with specific gains in speed and performance.
The paper tackles model parameter identification for autonomous racing vehicles by proposing a hyperparameter optimization scheme, achieving over 13 times faster convergence than traditional methods and enabling stable high-speed driving up to 217 km/h in field tests.
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.