Search arXivSearch

arXiv · 2601.05072

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

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuxin Wang, Yuankai He, Boyang Tian, Lichen Xian, Weisong Shi. 2026-01-23. DAVOS: An Autonomous Vehicle Operating System in the Vehicle Computing Era. https://arxiv.org/abs/2601.05072

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

TierBPF: Page Migration Admission Control for Tiered Memory via eBPF

Software-based memory tiering systems decide which pages to place on the slower or faster tier. However, they do not consider two important factors that greatly influence application performance: the size of the migrated pages, and the underlying hardware device and tiering topology. We introduce TierBPF, a software mechanism that can be plugged into existing memory tiering systems to take these factors into account, by making architecture-aware page admission decisions. TierBPF is implemented as a set of eBPF hooks, allowing users to define their custom policies. In order to make its decisions, TierBPF utilizes a lightweight tracking mechanism for page profiling which is not dependent on the application's working set size. TierBPF, integrated into three memory tiering systems and evaluated with 17 workloads, achieves geomean throughput gains of up to 15.9% with improvements of up to 75% for individual workloads.

cs.OS

Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems

AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of 290 public reports. Our study reveals two fundamental gaps: users and agents have limited information about filesystem effects and insufficient control over them. To close these gaps, we propose to shift information and control from agents to filesystems. We introduce agent-native filesystems and identify three primitives they should provide: introspect effects, undo mutations, and gate accesses. These primitives let agents operate autonomously while reserving user interaction for sensitive accesses and final review. We build YoloFS, an agent-native filesystem. YoloFS stages mutations until the user commits them, snapshots intermediate states for agent self-correction, and uses progressive permission to let users adapt access rules during execution. We evaluate YoloFS with a new methodology that captures interactions among the user, agent, and filesystem. On 11 tasks with hidden side effects, YoloFS enables agents to self-correct in 8 and stages all mutations for user review. On 112 routine tasks, YoloFS reduces user interaction while matching the baseline success rate. YoloFS is open-sourced at https://github.com/YoloFS/YoloFS.

cs.OS

Netkit: Specializing Linux Packet Delivery for Container Networks

Cloud-native microservices architectures rely on network namespaces for isolation, with the overhead of container communications remaining a critical performance bottleneck. While colocating containers on the same host mitigates some of this overhead, it cannot match the performance of communication within a single network namespace. Existing solutions either require application rewrites or fail to support the full Linux network stack expected by containerized applications. In this paper, we present netkit, an eBPF-based datapath that specializes the Linux networking stack to eliminate redundant backlog queue traversals during network namespace transitions. netkit leverages eBPF to transparently redirect packets between namespaces, bypassing unnecessary buffering while preserving compatibility with existing container applications. Our implementation in the Linux kernel, integrated with minimal changes to the Cilium network plugin for Kubernetes, improves throughput by up to 37\% and achieves parity between container-to-container and process-to-process communications, effectively closing the performance gap introduced by namespace isolation.

cs.OS