Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational constraint and complexity often limit the use of Model Predictive Control (MPC). As a consequence, geometric controllers are the most frequently deployed controllers. They prove to be performant while yielding implementation and operational simplicity. Yet, they inherently lack the incorporation of model dynamics, thus limiting the race car to a velocity domain where tire slip can be neglected. This paper presents Model- and Acceleration-based Pursuit (MAP) a high-performance model-based trajectory tracking algorithm that preserves the simplicity of geometric approaches while leveraging tire dynamics. The proposed algorithm allows accurate tracking of a trajectory at unprecedented velocities compared to State-of-the-Art (SotA) geometric controllers. The MAP controller is experimentally validated and outperforms the reference geometric controller four-fold in terms of lateral tracking error, yielding a tracking error of 0.055m at tested speeds up to 11m/s.

翻译：自动驾驶赛车是一个日益流行的研究领域，它推动了自动驾驶算法向极限发展，并成为通用自动驾驶的催化剂。对于缩尺自动驾驶赛车平台，计算约束和复杂性往往限制了模型预测控制（MPC）的使用。因此，几何控制器是最常用的控制器。它们被证明性能良好，同时易于实现和操作。然而，它们本质上缺乏对模型动力学的整合，从而将赛车限制在轮胎滑移可以忽略的速度范围内。本文提出了基于模型与加速度的追逐（MAP）——一种高性能基于模型的轨迹跟踪算法，该算法保持了几何方法的简洁性，同时利用了轮胎动力学。与最先进的（SotA）几何控制器相比，所提出的算法能够在前所未有的速度下精确跟踪轨迹。MAP控制器经过实验验证，在横向跟踪误差方面比参考几何控制器提升了四倍，在测试速度高达11m/s时产生0.055m的跟踪误差。