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：自主赛车是一个日益受到广泛关注的研究领域，它将自动驾驶算法推向极限，并成为通用自动驾驶技术的催化剂。对于缩比自主赛车平台而言，计算约束与复杂度常限制模型预测控制（MPC）的应用。因此，几何控制器成为最常用的控制方案，其兼具高性能与实施及操作简便性。然而，这类控制器本质上缺乏对模型动力学的整合，导致赛车仅能在轮胎滑移可忽略的速度范围内运行。本文提出基于模型与加速度的追踪算法（MAP），这是一种融合轮胎动力学的高性能基于模型轨迹跟踪算法，既保留了几何方法的简洁性，又实现了对轮胎动力学的利用。相比最先进的几何控制器（SotA），该算法能够在前所未有的速度下实现精准轨迹跟踪。通过实验验证，MAP控制器在横向跟踪误差上比参考几何控制器提升四倍，在最高11m/s测试速度下实现了0.055m的跟踪误差。