  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.
