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Autonomous racing is a challenging problem, as the vehicle needs to operateat 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 morechallenging is the accurate identification of vehicle model parameters thatdictate the effects of the lateral tire slip, which may change over time, forexample, due to wear and tear of the tires. Current works either propose modelidentification offline or need good parameters to start with (within 15-20\% ofactual value), which is not enough to account for major changes in tire modelthat occur during actual races when driving at the control limits. We propose aunified framework which learns the tire model online from the collected data,as well as adjusts the model based on environmental changes even if the modelparameters change by a higher margin. We demonstrate our approach in numericand high-fidelity simulators for a 1:43 scale race car and a full-size car.

Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine