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.

对人类驾驶员的良好理解和建模对于现代车辆开发至关重要，尤其是在赛车运动中，赛车应该完美地适应其驾驶员。同时，由于个人驾驶风格、复杂和不确定的决策过程以及较小的稳定性裕度，客观评估，尤其是模仿职业赛车手是困难的。在本文中，我们提出了一种整体方法，以稳健的方式识别和建模个人比赛驾驶风格。我们开发了驾驶员识别和度量排名算法(DIMRA) 作为一种基于数据的方法，用于对专业赛车手进行深入的客观分析和评估。在这些知识的支持下，我们扩展和调整了模仿学习框架驾驶员行为概率建模(ProMoD)，以便在复杂的模拟环境中对赛车手进行建模。对来自专业赛车手的数据进行的评估表明，DIMRA 能够导出描述人类驾驶风格的指标，以及 ProMoD 能够在专业赛车驾驶模拟器中通过类人控制稳健地生成具有竞争力的圈数。识别和模仿个人驾驶风格的能力不仅支持赛车的性能优化，还可以在未来的工作中帮助公路车和驾驶员辅助系统的开发。