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：自主赛车作为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，计算约束和复杂性往往限制了模型预测控制(MPC)的应用。因此，几何控制器成为最常用的控制器：它们被证明具有良好的性能，同时实现和操作简便。然而，这类控制器本质上缺乏对模型动力学的整合，从而将赛车的速度限制在轮胎滑移可忽略的范围内。本文提出了基于模型与加速度的追逐(MAP)算法——一种基于模型的高性能轨迹跟踪算法，它在保留几何方法简洁性的同时利用了轮胎动力学特性。与最先进的(SotA)几何控制器相比，所提算法能够在前所未有的速度下实现精确的轨迹跟踪。实验验证表明，该MAP控制器的横向跟踪误差是参考几何控制器的四分之一，在高达11米/秒的测试速度下，其跟踪误差仅为0.055米。