为自动驾驶汽车设计高效分层存储系统，解决海量数据存取难题。
AVS: A Computational and Hierarchical Storage System for Autonomous Vehicles
- 按数据模态压缩并分层存储，提升效率
- 实测日均14TB数据可快速检索且存储减半
- 适合自动驾驶系统开发与车载存储优化者
自动驾驶汽车正演变为移动计算平台，配备强大处理器和多种传感器，每日产生高达14TB的异构数据。支持第三方应用需具备通用性、可查询的车载存储系统，但现有数据记录器与存储栈难以高效处理。本文提出AVS，一种将计算与分层存储协同设计的系统：基于模态感知的数据压缩与归约、冷热分层与日级归档、轻量元数据索引层。基于真实车载数据的系统级基准测试覆盖SSD/HDD文件系统与嵌入式索引，验证于嵌入式硬件上的真实L4自动驾驶轨迹。原型实现可预测的实时写入、快速选择性读取，并在有限资源下显著减少存储占用。研究还提出扩展方向，推动存储成为自动驾驶架构中的核心组件。
原文摘要 · Abstract (English)
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.
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