Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

AROLA: A Modular Layered Architecture for Scaled Autonomous Racing

Fam Shihata, Mohammed Abdelazim, Ahmed Hussein

TL;DR

AROLA tackles fragmentation in scaled autonomous racing by introducing a modular, eight-layer architecture with standardized ROS 2 interfaces and an integrated Race Monitor for real-time logging and post-hoc benchmarking. The approach preserves interpretability while enabling rapid component interchange and systematic evaluation across sensing, perception, localization, planning, behavior, control, and actuation. Experimental validation on RoboRacer, including the 2025 IV25 competition, demonstrates competitive performance and demonstrates the ease of swapping modules and re-running experiments. Collectively, AROLA and Race Monitor offer a practical path toward reproducible, scalable development in autonomous racing, with clear benchmarks and cross-platform compatibility.

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.

AROLA: A Modular Layered Architecture for Scaled Autonomous Racing

TL;DR

AROLA tackles fragmentation in scaled autonomous racing by introducing a modular, eight-layer architecture with standardized ROS 2 interfaces and an integrated Race Monitor for real-time logging and post-hoc benchmarking. The approach preserves interpretability while enabling rapid component interchange and systematic evaluation across sensing, perception, localization, planning, behavior, control, and actuation. Experimental validation on RoboRacer, including the 2025 IV25 competition, demonstrates competitive performance and demonstrates the ease of swapping modules and re-running experiments. Collectively, AROLA and Race Monitor offer a practical path toward reproducible, scalable development in autonomous racing, with clear benchmarks and cross-platform compatibility.

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.
Paper Structure (21 sections, 6 figures, 4 tables)

This paper contains 21 sections, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Approach
  4. Fundamental Components
  5. Sensing
  6. Preprocessing
  7. Perception
  8. Localization and Mapping
  9. Planning
  10. Behavior
  11. Control
  12. Actuation
  13. Data Flow
  14. System Integration and Deployment
  15. Race Monitor
  16. ...and 6 more sections

Figures (6)

  • Figure 1: Overview of the complete AROLA architecture
  • Figure 2: Example system configuration using AROLA
  • Figure 3: Error-mapped trajectory showing deviations along the LQR controller path.
  • Figure 4: Lap-time trend for the LQR controller.
  • Figure 5: Per-axis translational error (XY) compared to the reference trajectory for the LQR controller (exp_011). Z remains constant.
  • ...and 1 more figures