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.
翻译:工程高性能赛车的工程高性能赛车需要直接考虑使用现实世界测试或Loop 人驾驶员的模拟来直接考虑人驾驶员。 除此之外,与类似人类的种族驾驶员模型进行离线模拟可以提高车辆开发过程的效能和效率,但由于各种挑战而难以获得这种过程。 通过这项工作,我们打算使人们更好地了解种族驾驶员的行为,并采用基于模仿学习的适应性人驾驶员模型。利用现有调查结果和与专业种族工程师的访谈,我们确定基本适应机制,以及驾驶员如何学会如何在新轨道上优化驾驶时间。随后,我们利用这些洞察力来为最近推出的概率驾驶员模型模型方法开发通用和适应技术,并使用专业种族驾驶员和最先进的赛车模拟器的数据对其进行评估。我们展示了我们的框架可以在看不见的赛道上创造现实的行车线分布,其性能几乎像人类一样。 此外,我们的驾驶员模型通过大腿优化驾驶舱,纠正前几圈的驾驶错误,同时加快行驶时速度。这项工作有助于更好地了解和模拟人类驾驶员的自动化车动技术,从而加速模拟方法。