Cloud-Native Operation of Roadside Infrastructure Enabling Demand-Driven Collective Perception via V2X

TL;DR

This paper introduces a cloud-native, demand-driven architecture for roadside infrastructure in intelligent transport systems, enabling efficient, scalable, and energy-saving operation of collective perception applications using V2X technology.

Contribution

It presents a novel cloud-native, demand-driven orchestration approach for roadside infrastructure, validated through real-world experiments demonstrating improved scalability and energy efficiency.

Findings

The collective perception application activates timely for passing vehicles.

Demand-driven operation reduces energy consumption and hardware wear.

Real-world tests confirm the feasibility of the proposed architecture.

Abstract

Intelligent roadside infrastructure is a key enabler for cooperative intelligent transport systems (C-ITS), supporting vehicles equipped with automated driving systems (ADS), e.g., through enhanced environment perception. With a growing number and an expanding functional scope of roadside units, scalable and efficient operation becomes a challenge. This paper presents a cloud-native architecture for the operation of distributed roadside infrastructure based on a Kubernetes cluster spanning roadside units and a cloud server. Building on this architecture, a demand-driven orchestration approach is implemented to dynamically deploy resource-intensive services only when required. As a representative use case, a V2X-based collective perception application is deployed on-demand when a connected vehicle is nearby. The approach is validated in a real-world experiment in our test field in Aachen, demonstrating that the collective perception application starts in time for the vehicle to benefit from it. Without any demand, the application remains inactive, reducing energy consumption, channel congestion, and hardware wear. Beyond the primary evaluation, V2X recordings from the test field are analyzed to estimate the energy-saving potential of demand-driven operation. In summary, the results demonstrate the practical feasibility of cloud-native, demand-driven operation of roadside infrastructure and indicate its potential to improve scalability and (energy) efficiency in future C-ITS deployments.