  Engineering a high-performance race car requires a direct consideration of
the human driver using real-world tests or Human-Driver-in-the-Loop
simulations. Apart from that, offline simulations with human-like race driver
models could make this vehicle development process more effective and efficient
but are hard to obtain due to various challenges. With this work, we intend to
provide a better understanding of race driver behavior and introduce an
adaptive human race driver model based on imitation learning. Using existing
findings and an interview with a professional race engineer, we identify
fundamental adaptation mechanisms and how drivers learn to optimize lap time on
a new track. Subsequently, we use these insights to develop generalization and
adaptation techniques for a recently presented probabilistic driver modeling
approach and evaluate it using data from professional race drivers and a
state-of-the-art race car simulator. We show that our framework can create
realistic driving line distributions on unseen race tracks with almost
human-like performance. Moreover, our driver model optimizes its driving lap by
lap, correcting driving errors from previous laps while achieving faster lap
times. This work contributes to a better understanding and modeling of the
human driver, aiming to expedite simulation methods in the modern vehicle
development process and potentially supporting automated driving and racing
technologies.
