AROLA: A Modular Layered Architecture for Scaled Autonomous Racing

2026-02-02

AI Summary

This work proposes AROLA, a modular and layered architecture for autonomous racing built upon ROS 2, addressing the limitations of existing fragmented or monolithic systems that lack standardized interfaces and hinder rapid component swapping and objective performance evaluation. AROLA decouples the autonomous driving pipeline into standardized functional layers—including perception, localization, planning, and control—and integrates a lightweight Race Monitor framework to enable real-time data logging and standardized post-race analysis. By enforcing uniform interfaces, the architecture supports plug-and-play module integration and facilitates reproducible benchmarking, significantly enhancing development efficiency and experimental comparability. The proposed system has been validated on both the RoboRacer simulation and physical platforms and was successfully deployed in the RoboRacer IV25 competition in 2025.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Autonomous racing has advanced rapidly, particularly on scaled platforms, and software stacks must evolve accordingly. In this work, AROLA is introduced as a modular, layered software architecture in which fragmented and monolithic designs are reorganized into interchangeable layers and components connected through standardized ROS 2 interfaces. The autonomous-driving pipeline is decomposed into sensing, pre-processing, perception, localization and mapping, planning, behavior, control, and actuation, enabling rapid module replacement and objective benchmarking without reliance on custom message definitions. To support consistent performance evaluation, a Race Monitor framework is introduced as a lightweight system through which lap timing, trajectory quality, and computational load are logged in real time and standardized post-race analyses are generated. AROLA is validated in simulation and on hardware using the RoboRacer platform, including deployment at the 2025 RoboRacer IV25 competition. Together, AROLA and Race Monitor demonstrate that modularity, transparent interfaces, and systematic evaluation can accelerate development and improve reproducibility in scaled autonomous racing.

Problem

• autonomous racing
• software architecture
• modularity
• benchmarking
• reproducibility

Innovation

• modular architecture
• ROS 2
• autonomous racing
• benchmarking
• Race Monitor