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Algorithm Deployment Optimization

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Computing Systems for Autonomous Driving

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Abstract

With the burgeoning growth of the Internet of Everything (IoE), the amount of data generated by these edge devices has increased dramatically. Terabytes of sensor data could be generated per vehicle per day, which makes it impossible for uploading them into the cloud and process. On the other hand, with more and more computation resources deployed on the vehicle, lots of researches embrace the design which incorporate cloud and edge computing. However, the algorithm deployment of algorithms like DNNs still faces several challenges: one is the data volume, another is data privacy. What role should the edge play to support CAVs applications? In this chapter, we introduce two cloud-edge collaboration systems to support CAVs applications. One is CLONE, which is a collaborative learning setting on the edges (CLONE) which is able to mainly demonstrate the effectiveness of latency reduction and privacy-preserving. The other is pBEAM, which is a collaborative cloud-edge computation system for personalized driving behavior modeling.

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  1. Department of Computer Science, Wayne State University, Detroit, MI, USA

    Weisong Shi & Liangkai Liu

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Shi, W., Liu, L. (2021). Algorithm Deployment Optimization. In: Computing Systems for Autonomous Driving. Springer, Cham. https://doi.org/10.1007/978-3-030-81564-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-81564-6_3

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