Identification and modelling of race driving styles

Abstract

A good understanding and modelling of the human driver is essential for modern vehicle development, particularly in motorsports, where the race car should fit its driver perfectly. At the same time, an objective assessment and especially imitation of professional race drivers is difficult due to individual driving styles, complex and non-deterministic decision making processes, and small stability margins. In this paper, we present a holistic approach to identify and model individual race driving styles in a robust way. We develop the Driver Identification and Metric Ranking Algorithm (DIMRA) as a data-based method for an in-depth objective analysis and assessment of professional race drivers. Supported by this knowledge, we extend and adapt the imitation learning framework Probabilistic Modeling of Driver Behavior (ProMoD) in order to model race drivers in a complex simulation environment. An evaluation with data from professional race drivers shows the capability of DIMRA to derive metrics which describe human race driving styles, as well as ProMoD to robustly generate competitive laps with human-like controls in a professional motorsport driving simulator. The ability to identify and imitate individual driving styles does not only support the performance optimisation of race cars but could also aid the development of road cars and driver assistance systems in future work.