Toward Fair and Thrilling Autonomous Racing: Governance Rules and Performance Metrics for the Autonomous One
Bai Li, Y. Fang, Tian’ao Xu, Siji Ma, Haorui Wang, Yazhou Wang, Xinyuan Li, Tantan Zhang, Xuepeng Bian, Fei–Yue Wang
Abstract
This letter is the second report from a series of IEEE TIV's Decentralized and Hybrid Workshops (DHWs) on Intelligent Vehicles for Education (IV4E). It outlines the prospect of The Autonomous One (TAO), a future autonomous racing competition modeled after Formula One to advance IV4E by pushing the boundaries of artificial intelligence. Existing autonomous races face challenges, including compromised fairness and low participant enthusiasm. These issues limit spectator engagement, thereby reducing the races' educational value. To prevent similar flaws in organizing TAO, our focus lies in setting governance rules from an organizer's perspective. In these DHWs, we analyzed the rules of existing autonomous races and suggested rule-making guidelines for TAO. To improve fairness, we recommend a balanced scoring system with rigorous monitoring of participating teams' performances. To discourage consistent victories by a single team, we suggest that leading teams share their source codes, thereby setting the championship-level performance as the baseline in the next season and accelerating the evolution of all participating teams' abilities. For enhancing suspense and spectator interest, an on-site adaptive rewarding scheme should be deployed to create thrilling turnarounds. Our strategies aim to maintain fairness, increase spectator interest, inspire competitiveness, and ultimately contribute to the advancement of IV4E.