  Autonomous Emergency Braking (AEB) potentially brings significant
improvements in automotive safety due to its ability to autonomously prevent
collisions in situations where the driver may not be able to do so. Driven by
the poor performance of the state of the art in recent testing, this work
provides an online solution to identify critical parameters such as the current
and maximum friction coefficients. The method introduced here, namely Torque
Vectoring for Active Learning (TVAL), can perform state and parameter
estimation whilst following the driver's input. Importantly with less power
requirements than normal driving. Our method is designed with a crucial focus
on ensuring minimal disruption to the driver, allowing them to maintain full
control of the vehicle. Additionally, we exploit a rain/light sensor to drive
the observer resampling to maintain estimation certainty across prolonged
operation. Then a scheme to modulate TVAL is introduced that considers
powertrain efficiency, safety, and availability in an online fashion. Using a
high-fidelity vehicle model and drive cycle we demonstrate the functionality of
TVAL controller across changing road surfaces where we successfully identify
the road surface whenever possible.
