  Professional race drivers are still superior to automated systems at
controlling a vehicle at its dynamic limit. Gaining insight into race drivers'
vehicle handling process might lead to further development in the areas of
automated driving systems. We present a method to study and evaluate race
drivers on a driver-in-the-loop simulator by analysing tire grip potential
exploitation. Given initial data from a simulator run, two optimiser based on
physical models maximise the horizontal vehicle acceleration or the tire
forces, respectively. An overall performance score, a vehicle-trajectory score
and a handling score are introduced to evaluate drivers. Our method is thereby
completely track independent and can be used from one single corner up to a
large data set. We apply the proposed method to a motorsport data set
containing over 1200 laps from seven professional race drivers and two amateur
drivers whose lap times are 10-20% slower. The difference to the professional
drivers comes mainly from their inferior handling skills and not their choice
of driving line. A downside of the presented method for certain applications is
an extensive computation time. Therefore, we propose a Long-short-term memory
(LSTM) neural network to estimate the driver evaluation scores. We show that
the neural network is accurate and robust with a root-mean-square error between
2-5% and can replace the optimisation based method. The time for processing the
data set considered in this work is reduced from 68 hours to 12 seconds, making
the neural network suitable for real-time application.
