Publikation
An Adaptive Human Driver Model for Realistic Race Car Simulations
Stefan Löckel; Siwei Ju; Maximilian Schaller; Peter van Vliet; Jan Peters
In: IEEE Transactions on Systems, Man and Cybernetics, Vol. 53, No. 11, Pages 6718-6730, arXiv, 2023.
Zusammenfassung
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.