Accelerating Autonomy: Insights from Pro Racers in the Era of Autonomous Racing - An Expert Interview Study

TL;DR

This study explores professional racing drivers' expertise and strategies to inform the development of more adaptive autonomous racing algorithms, highlighting human approaches to vehicle limit exploration and lap time minimization.

Contribution

It provides qualitative insights into drivers' techniques and skills, contrasting them with autonomous software capabilities to guide new algorithm development.

Findings

Drivers use exploration strategies to reach vehicle limits.

Human techniques differ from autonomous software approaches.

Insights enable development of more adaptive autonomy modules.

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.