Scalable Supervisory Architecture for Autonomous Race Cars

TL;DR

This paper introduces a modular, scalable supervisory architecture for autonomous race cars that supports diverse configurations and strategies, validated through successful competition participation and consistent performance across environments.

Contribution

It presents a novel scalable architecture for autonomous racing that emphasizes modularity and adaptability, enabling supervision of multiple strategies and configurations.

Findings

Consistent racing performance across different environments

Successful participation in two major competitions

Architecture's scalability and versatility validated

Abstract

In recent years, the number and importance of autonomous racing leagues, and consequently the number of studies on them, has been growing. The seamless integration between different series has gained attention due to the scene's diversity. However, the high cost of full scale racing makes it a more accessible development model, to research at smaller form factors and scale up the achieved results. This paper presents a scalable architecture designed for autonomous racing that emphasizes modularity, adaptability to diverse configurations, and the ability to supervise parallel execution of pipelines that allows the use of different dynamic strategies. The system showcased consistent racing performance across different environments, demonstrated through successful participation in two relevant competitions. The results confirm the architecture's scalability and versatility, providing a robust foundation for the development of competitive autonomous racing systems. The successful application in real-world scenarios validates its practical effectiveness and highlights its potential for future advancements in autonomous racing technology.