Predictive Network Configuration with Hierarchical Spectral Clustering for Software Defined Vehicles

TL;DR

This paper introduces a dynamic network configuration method for Software Defined Vehicles that uses hierarchical spectral clustering to optimize resource allocation and QoS in Ethernet-based in-vehicle networks.

Contribution

It proposes a novel configuration generation approach that dynamically switches between pre-computed offboard configurations for better network management in SDVs.

Findings

Simulation results demonstrate improved network resource utilization.

The method ensures better QoS guarantees for network flows.

Dynamic switching reduces configuration overhead.

Abstract

The increasing connectivity and autonomy of vehicles has led to a growing need for dynamic and real-time adjustments to software and network configurations. Software Defined Vehicles (SDV) have emerged as a potential solution to adapt to changing user needs with continuous updates and onboard reconfigurations to offer infotainment, connected, and background services such as cooperative driving. However, network configuration management in SDVs remains a significant challenge, particularly in the context of shared Ethernet-based in-vehicle networks. Traditional worst-case static configuration methods cannot efficiently allocate network resources while ensuring Quality of Service (QoS) guarantees for each network flow within the physical topology capabilities. In this work, we propose a configuration generation methodology that addresses these limitations by dynamically switching between pre-computed offboard configurations downloaded to the vehicle. Simulation results are presented and future work is discussed.