Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require highly accurate perception, state estimation, planning and precise application of controls. What makes it even more challenging is the accurate identification of vehicle model parameters that dictate the effects of the lateral tire slip, which may change over time, for example, due to wear and tear of the tires. Current works either propose model identification offline or need good parameters to start with (within 15-20\% of actual value), which is not enough to account for major changes in tire model that occur during actual races when driving at the control limits. We propose a unified framework which learns the tire model online from the collected data, as well as adjusts the model based on environmental changes even if the model parameters change by a higher margin. We demonstrate our approach in numeric and high-fidelity simulators for a 1:43 scale race car and a full-size car.

翻译：自主赛车是一项极具挑战性的问题，因为车辆需要工作在摩擦或操控极限状态下以实现最小圈时。自主赛车需要高精度的感知、状态估计、规划以及精确的控制应用。更具挑战性的是准确辨识决定侧向轮胎滑移效应的车辆模型参数，这些参数可能随时间变化（例如，因轮胎磨损）。现有研究要么提出离线模型辨识方法，要么需要初始参数接近真实值（偏差在15-20%以内），但这种方法无法应对实际比赛中车辆在控制极限行驶时轮胎模型参数发生剧烈变化的情况。我们提出一个统一框架，该框架从采集数据中在线学习轮胎模型，并能根据环境变化调整模型——即使模型参数变化幅度较大。我们在数值仿真和高保真仿真器中分别对1:43比例赛车和全尺寸赛车验证了该方法。