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.

翻译：在本论文中，我们提出了一种通过超参数优化方案实现的模型参数辨识方法（MI-HPO）。该方法采用高效的探索-利用策略，以数据驱动优化方式辨识动力学模型参数。我们将该方法应用于全尺寸自主赛车AV-21的模型参数辨识，并将优化后的参数集成到基于模型的平台规划与控制系统设计中。实验结果表明，MI-HPO的收敛速度比传统参数辨识方法快13倍以上。此外，通过MI-HPO学习的参数化模型对给定数据集表现出良好的拟合度，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实地测试以验证基于模型的系统，在印第安纳波利斯赛车场和拉斯维加斯赛车场实现了稳定的避障能力与高达217公里/小时的高速行驶。本研究源代码及测试视频可在 https://github.com/hynkis/MI-HPO 获取。