# Fair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systems

arXiv: https://arxiv.org/abs/2503.03774 · HTML: https://mlsanity.com/paper/2503.03774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.07971/) (2020)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [A Non-Cooperative Game Approach to Autonomous Racing](https://mlsanity.com/paper/1712.03913/) (2017)
- [Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving](https://mlsanity.com/paper/2207.03673/) (2022)
- [Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments](https://mlsanity.com/paper/2104.09705/) (2021)
- [Outracing Human Racers with Model-based Planning and Control for Time-trial Racing](https://mlsanity.com/paper/2211.09378/) (2022)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing](https://mlsanity.com/paper/2607.20973/) (2026)

## Abstract (arXiv)

Autonomous racing has gained significant attention as a platform for high-speed decision-making and motion control. While existing methods primarily focus on trajectory planning and overtaking strategies, the role of sportsmanship in ensuring fair competition remains largely unexplored. In human racing, rules such as the one-motion rule and the enough-space rule prevent dangerous and unsportsmanlike behavior. However, autonomous racing systems often lack mechanisms to enforce these principles, potentially leading to unsafe maneuvers. This paper introduces a bi-level game-theoretic framework to integrate sportsmanship (SPS) into versus racing. At the high level, we model racing intentions using a Stackelberg game, where Monte Carlo Tree Search (MCTS) is employed to derive optimal strategies. At the low level, vehicle interactions are formulated as a Generalized Nash Equilibrium Problem (GNEP), ensuring that all agents follow sportsmanship constraints while optimizing their trajectories. Simulation results demonstrate the effectiveness of the proposed approach in enforcing sportsmanship rules while maintaining competitive performance. We analyze different scenarios where attackers and defenders adhere to or disregard sportsmanship rules and show how knowledge of these constraints influences strategic decision-making. This work highlights the importance of balancing competition and fairness in autonomous racing and provides a foundation for developing ethical and safe AI-driven racing systems.