Deterministic Task Scheduling in In-Vehicle Networks for Software-Defined Vehicles

2026-04-17

AI Summary

This work addresses the challenge that existing approaches fail to meet the stringent requirements of deterministic service and reliable resource scheduling for critical functions in software-defined vehicles. Targeting zonal electronic/electrical (E/E) architectures, the paper proposes a novel deterministic task scheduling method for in-vehicle networks that overcomes the limitations of conventional shortest-path or minimum-execution-time strategies. The approach achieves high determinism and load balancing across both wired and hybrid wired-wireless topologies by integrating centralized compute nodes with the zonal E/E architecture and employing a customized scheduling algorithm to jointly optimize task allocation and resource management. Experimental results demonstrate significant improvements in service determinism, resource utilization, and system scalability across diverse in-vehicle network topologies.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Modern vehicles are embedding increasing levels of automation, connectivity, and intelligence, which require advanced in-vehicle networks and computational platforms to support the dependability and deterministic requirements of critical in-vehicle functions. To this end, the automotive industry is shifting towards software-defined vehicles (SDVs) and zonal E/E architectures with centralized computing nodes. Realizing the full potential of these new architectures requires an efficient management of the in-vehicles computational workload. In this context, this paper introduces a deterministic task scheduling approach for in-vehicle networks (IVN), and demonstrates that it can better guarantee deterministic service levels than alternative approaches based on the shortest path or the objective to minimize task execution time. Our evaluation also demonstrates that a deterministic task scheduling can satisfactorily support increasing in-vehicle computational workloads and tasks, and achieve a more balanced workload and resource utilization across the IVN. These gains are validated across a variety of IVN topologies, and in hybrid wireless-wired IVN implementations, where a gradual introduction of wireless offers increased in-vehicle connectivity diversity.

Problem

• Deterministic Task Scheduling
• In-Vehicle Networks
• Software-Defined Vehicles
• Workload Management
• Deterministic Service

Innovation

• deterministic task scheduling
• software-defined vehicles
• in-vehicle networks
• zonal E/E architecture
• workload balancing