# ROS 2-Based Architecture for Autonomous Driving Systems: Design and Implementation

## TL;DR

This paper introduces a modular and scalable architecture for autonomous vehicles using ROS 2, validated on a 1:10-scale test vehicle with real-time performance.

## Contribution

A lightweight, ROS 2-based architecture for autonomous driving that ensures real-time performance and fault containment.

## Key findings

- The architecture met worst-case deadlines in perception and planning tasks under stress.
- The design supports heterogeneous hardware and provides deterministic behavior.
- Validation on a 1:10-scale vehicle demonstrated compliance with real-time standards.

## Abstract

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement.