A Containerized Microservice Architecture for a ROS 2 Autonomous Driving Software: An End-to-End Latency Evaluation

TL;DR

This paper evaluates how containerized microservice architectures impact real-time performance metrics in autonomous driving systems, demonstrating latency improvements over traditional deployments.

Contribution

It introduces a containerized microservice architecture for ROS 2 autonomous driving and provides a comprehensive latency evaluation showing performance benefits.

Findings

Mean end-to-end latency improved by 5-8%.

Maximum latencies were significantly reduced.

Containerization benefits observed over bare-Linux deployments.

Abstract

The automotive industry is transitioning from traditional ECU-based systems to software-defined vehicles. A central role of this revolution is played by containers, lightweight virtualization technologies that enable the flexible consolidation of complex software applications on a common hardware platform. Despite their widespread adoption, the impact of containerization on fundamental real-time metrics such as end-to-end latency, communication jitter, as well as memory and CPU utilization has remained virtually unexplored. This paper presents a microservice architecture for a real-world autonomous driving application where containers isolate each service. Our comprehensive evaluation shows the benefits in terms of end-to-end latency of such a solution even over standard bare-Linux deployments. Specifically, in the case of the presented microservice architecture, the mean end-to-end latency can be improved by 5-8 %. Also, the maximum latencies were significantly reduced using container deployment.