The control for aggressive driving of autonomous cars is challenging due to the presence of significant tyre slip. Data-driven and mechanism-based methods for the modeling and control of autonomous cars under aggressive driving conditions are limited in data efficiency and adaptability respectively. This paper is an attempt toward the fusion of the two classes of methods. By means of a modular design that is consisted of mechanism-based and data-driven components, and aware of the two-timescale phenomenon in the car model, our approach effectively improves over previous methods in terms of data efficiency, ability of transfer and final performance. The hybrid mechanism-and-data-driven approach is verified on TORCS (The Open Racing Car Simulator). Experiment results demonstrate the benefit of our approach over purely mechanism-based and purely data-driven methods.

翻译：自动驾驶汽车在激进驾驶条件下的控制因显著的轮胎滑移而具有挑战性。在激进驾驶条件下，数据驱动方法和基于机制的方法分别在数据效率和适应性方面存在局限性。本文尝试融合这两类方法。通过采用由机制组件和数据驱动组件组成的模块化设计，并考虑汽车模型中的两时间尺度现象，我们的方法在数据效率、迁移能力和最终性能方面相较于先前方法有显著提升。这种混合机制与数据驱动的方法在TORCS（开源赛车模拟器）上进行了验证。实验结果表明，我们的方法优于纯基于机制和纯数据驱动的方法。