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

2026-05-20

AI Summary

This work addresses the scalability and energy efficiency challenges arising from the expanding scale and functionality of roadside units (RSUs) in vehicular networks. To tackle these issues, the authors propose a cloud-native architecture that uniquely integrates Kubernetes clusters with on-demand service orchestration across cloud and edge infrastructures. This approach enables dynamic deployment of resource-intensive services—such as V2X cooperative perception—activating them only when vehicles are in proximity and automatically suspending them during idle periods. Empirical evaluations demonstrate that the system can reliably launch applications just before vehicle arrival, significantly reducing energy consumption, channel congestion, and hardware wear while enhancing overall system scalability and operational efficiency.

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.

Problem

• cloud-native
• roadside infrastructure
• demand-driven
• collective perception
• V2X

Innovation

• cloud-native
• demand-driven orchestration
• V2X collective perception
• Kubernetes
• roadside infrastructure