Proceedings ArticleDOI
HydraSpace: Computational Data Storage for Autonomous Vehicles
Ruijun Wang,Liangkai Liu,Weisong Shi +2 more
- pp 70-77
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TLDR
In this article, the authors proposed a computational storage system called HydraSpace with multi-layered storage architecture and practical compression algorithms to manage the sensor pipe data, and discussed five open questions related to the challenge of storage design for autonomous vehicles.Abstract:
To ensure the safety and reliability of an autonomous driving system, multiple sensors have been installed in various positions around the vehicle to eliminate any blind point which could bring potential risks. Although the sensor data is quite useful for localization and perception, the high volume of these data becomes a burden for on-board computing systems. More importantly, the situation will worsen with the demand for increased precision and reduced response time of self-driving applications. Therefore, how to manage this massive amount of sensed data has become a big challenge. The existing vehicle data logging system cannot handle sensor data because both the data type and the amount far exceed its processing capability. In this paper, we propose a computational storage system called HydraSpace with multi-layered storage architecture and practical compression algorithms to manage the sensor pipe data, and we discuss five open questions related to the challenge of storage design for autonomous vehicles. According to the experimental results, the total reduction of storage space is achieved by 88.6% while maintaining the comparable performance of the self-driving applications.read more
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References
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Journal ArticleDOI
Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.
Journal ArticleDOI
The discrete wavelet transform: wedding the a trous and Mallat algorithms
TL;DR: It is shown that the commonly used Lagrange a trous filters are in one-to-one correspondence with the convolutional squares of the Daubechies filters for orthonormal wavelets of compact support.
Journal ArticleDOI
A Fast Computational Algorithm for the Discrete Cosine Transform
TL;DR: A Fast Discrete Cosine Transform algorithm has been developed which provides a factor of six improvement in computational complexity when compared to conventional DiscreteCosine Transform algorithms using the Fast Fourier Transform.
Journal ArticleDOI
Image coding using vector quantization: a review
Nasser M. Nasrabadi,R.A. King +1 more
TL;DR: First, the concept of vector quantization is introduced, then its application to digital images is explained, and the emphasis is on the usefulness of the vector quantification when it is combined with conventional image coding techniques, orWhen it is used in different domains.
Proceedings ArticleDOI
GHT: a geographic hash table for data-centric storage
TL;DR: This paper describes GHT, a Geographic Hash Table system for DCS on sensornets, and demonstrates that GHT is the preferable approach for the application workloads predicted, offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile.