AVS: A Computational and Hierarchical Storage System for Autonomous Vehicles

TL;DR

This paper introduces AVS, a hierarchical storage system for autonomous vehicles that efficiently manages massive heterogeneous data, enabling real-time processing, fast retrieval, and reduced storage footprint.

Contribution

AVS is a novel storage system co-designed with computation, featuring hierarchical layout, modality-aware compression, tiering, and lightweight indexing for AV data.

Findings

Predictable real-time data ingestion

Fast selective data retrieval

Significant storage footprint reduction

Abstract

Autonomous vehicles (AVs) are evolving into mobile computing platforms, equipped with powerful processors and diverse sensors that generate massive heterogeneous data, for example 14 TB per day. Supporting emerging third-party applications calls for a general-purpose, queryable onboard storage system. Yet today's data loggers and storage stacks in vehicles fail to deliver efficient data storage and retrieval. This paper presents AVS, an Autonomous Vehicle Storage system that co-designs computation with a hierarchical layout: modality-aware reduction and compression, hot-cold tiering with daily archival, and a lightweight metadata layer for indexing. The design is grounded with system-level benchmarks on AV data that cover SSD and HDD filesystems and embedded indexing, and is validated on embedded hardware with real L4 autonomous driving traces. The prototype delivers predictable real-time ingest, fast selective retrieval, and substantial footprint reduction under modest resource budgets. The work also outlines observations and next steps toward more scalable and longer deployments to motivate storage as a first-class component in AV stacks.