An Adaptive Human Driver Model for Realistic Race Car Simulations

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. Alternatively, 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 from expert knowledge and introduce an adaptive human race driver model based on imitation learning. Using existing findings in the literature, complemented with an interview with a race engineer, we identify fundamental adaptation mechanisms and how drivers learn to optimize lap time on a new track. Subsequently, we select the most distinct adaptation mechanisms via a survey with 12 additional experts, 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.