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:在这封信中,我们通过超参数优化计划(MIHO)提出一个示范参数识别方法。我们的方法采用高效的探索利用战略,以数据驱动优化的方式确定动态模型的参数。我们使用MIHO来模拟AV-21的参数识别模型,AV-21是一个全尺寸自主的种族车辆。我们随后将优化参数纳入平台基于模型的规划和控制系统的设计中。在实验中,MIHO表现出比传统参数识别方法更快13倍多的趋同率。此外,通过MIHO所学的参数模型显示,对特定数据集非常适合,在不可见的动态情景中显示一般化能力。我们进一步进行了广泛的实地测试,以验证基于模型的系统,显示在印第安纳波利斯汽车速度和拉斯维加斯汽车速度上稳定的障碍避免和高速驾驶至217公里/小时。MIHO的源码和测试录像见https://github.com/hynkis/MIHO。</s>