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.

翻译：摘要：本研究旨在探究职业赛车手的专业技能，以理解其认知与适应能力，从而开发新型自主算法。我们针对来自各大知名赛车联盟的11名职业车手、数据分析师及赛车教练开展了一项专家访谈研究。访谈采用探索性、非标准化的专家访谈形式，依托预设问题清单进行引导。研究考察了车手探索车辆极限的策略，并将其与当前最先进的自主赛车软件栈的能力进行对比。受访者被问及如何运用技术及技能快速接近并操控车辆极限，最终实现单圈时间最小化。访谈分析基于迈林定性内容分析框架展开，该框架将数据归纳至多个主次类别。研究结果揭示了人类在逼近车辆极限与缩短圈速方面的行为特征。基于这些发现，我们提出开发新型自主软件模块，以实现更具适应性的车辆行为。通过强调手动驾驶与自动驾驶技术之间的显著差异，本文鼓励进一步研究人类驾驶员最大化车辆性能的策略。