DAVOS: An Autonomous Vehicle Operating System in the Vehicle Computing Era

2026-01-08

arXiv.org

AI Summary

Existing in-vehicle operating systems struggle to simultaneously meet the stringent demands of real-time autonomous driving and data-driven services in terms of safety, efficiency, and scalability. This work proposes a unified in-vehicle operating system architecture that, for the first time, integrates real-time control and scalable data-intensive computing within a single framework. Through a co-designed approach, the system combines real-time scheduling, data isolation, efficient sensor processing, and modular extensibility. It enables coordinated management of safety-critical driving tasks and data-intensive applications, significantly enhancing system consistency, safety, and overall performance. The resulting platform provides intelligent vehicles with an efficient, scalable, and robust computing foundation capable of supporting the dual requirements of deterministic control and high-throughput data processing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Scalability of ML Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Vehicle computing represents a fundamental shift in how autonomous vehicles are designed and deployed, transforming them from isolated transportation systems into mobile computing platforms that support both safety-critical, real-time driving and data-centric services. In this setting, vehicles simultaneously support real-time driving pipelines and a growing set of data-driven applications, placing increased responsibility on the vehicle operating system to coordinate computation, data movement, storage, and access. These demands highlight recurring system considerations related to predictable execution, data and execution protection, efficient handling of high-rate sensor data, and long-term system evolvability, commonly summarized as Safety, Security, Efficiency, and Extensibility (SSEE). Existing vehicle operating systems and runtimes address these concerns in isolation, resulting in fragmented software stacks that limit coordination between autonomy workloads and vehicle data services. This paper presents DAVOS, the Dependable Autonomous Vehicle Operating System, a unified vehicle operating system architecture designed for the vehicle computing context. DAVOS provides a cohesive operating system foundation that supports both real-time autonomy and extensible vehicle computing within a single system framework.

Problem

• Autonomous Vehicle Operating System
• Vehicle Computing
• Safety
• Security
• Extensibility

Innovation

• Autonomous Vehicle Operating System
• Vehicle Computing
• Real-time Autonomy
• Data-centric Services
• SSEE