Abstract
This research aims to investigate professional racing drivers' expertise to develop an understanding of their cognitive and adaptive skills to create new autonomy algorithms. An expert interview study was conducted with 11 professional race drivers, data analysts, and racing instructors from across prominent racing leagues. The interviews were conducted using an exploratory, non-standardized expert interview format guided by a set of prepared questions. The study investigates drivers' exploration strategies to reach their vehicle limits and contrasts them with the capabilities of state-of-the-art autonomous racing software stacks. Participants were questioned about the techniques and skills they have developed to quickly approach and maneuver at the vehicle limit, ultimately minimizing lap times. The analysis of the interviews was grounded in Mayring's qualitative content analysis framework, which facilitated the organization of the data into multiple categories and subcategories. Our findings create insights into human behavior regarding reaching a vehicle's limit and minimizing lap times. We conclude from the findings the development of new autonomy software modules that allow for more adaptive vehicle behavior. By emphasizing the distinct nuances between manual and autonomous driving techniques, the paper encourages further investigation into human drivers' strategies to maximize their vehicles' capabilities.
Abstract (translated)
这项研究旨在调查职业赛车手的专业技能，以了解他们的认知和适应能力，从而创建新的自动驾驶算法。通过对11名职业赛车手、数据分析师和赛车教练的深入访谈，采用一种指导有预备问题的非标准化专家访谈形式进行研究。研究探讨了赛车手如何探索并操纵车辆极限，并与最先进的自动驾驶赛车软件栈的性能进行了比较。参与者还被问到他们为快速接近和操纵车辆极限所开发的技术和技能，最终减少了 lap time。分析访谈数据基于 Mayring 的定性内容分析框架，该框架有助于将数据组织成多个类别和子类别。我们的研究结果揭示了关于达到车辆极限和最小化 lap time 的人类行为。我们得出结论，新型的自动驾驶软件模块的发展允许更适应车辆的行为。通过强调手动和自动驾驶技术之间的显著差异，论文鼓励进一步研究人类驾驶员如何最大化车辆的功能。
URL
https://arxiv.org/abs/2405.02620
