In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (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, MIHO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MIHO 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 MIHO and videos of the tests are available at https://github.com/hynkis/MIHO.

翻译：本文提出了一种基于超参数优化方案的模型参数辨识方法（MIHO）。该方法采用高效的探索-利用策略，以数据驱动的优化方式辨识动力学模型的参数。我们利用MIHO对全尺寸自主赛车AV-21进行模型参数辨识，并将优化后的参数用于平台基于模型的规划与控制系统的设计。实验表明，MIHO的收敛速度比传统参数辨识方法快13倍以上。此外，通过MIHO学习得到的参数化模型对给定数据集具有良好的拟合性，并在未见过的动态场景中展现出泛化能力。我们进一步开展了广泛的场地测试，验证了基于模型的系统在印第安纳波利斯高速赛道和拉斯维加斯高速赛道上实现稳定的障碍物规避和高达217公里/小时的高速行驶能力。MIHO的源代码及测试视频可从https://github.com/hynkis/MIHO获取。