research-article

Collaborative cloud-edge computation for personalized driving behavior modeling

Authors:

Xingzhou Zhang,

Weisong ShiAuthors Info & Claims

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

Pages 209 - 221

https://doi.org/10.1145/3318216.3363310

Published: 07 November 2019 Publication History

Abstract

Driving behavior modeling is an essential component of Advanced Driver Assistance Systems (ADAS). Existing methods usually analyze driving behaviors based on generic driving data, which do not consider personalization and user privacy. In this paper, we propose pBEAM, a collaborative cloud-edge computation system for personalized driving behavior modeling. The driving behavior model is built on top of Generative Adversarial Recurrent Neural Networks (GARNN), which adapts to the dynamic change of normal driving. Transfer learning from cloud to edge improves the model performance and robustness on the edge. We prune the deep neural networks in the cloud in order to minimize the model transferring load while maximally preserve the original model performance. A personalized edge model is trained on top of the pruned model using CGARNN-Edge (Conditional GARNN), which considers drivers' personal or contextual information as additional conditions. User privacy is well protected as no personal data needs to be uploaded to the cloud. Experimental results on driving data from both real world and driving simulator show that the proposed CGARNN-Edge achieves the best performance among all the methods.

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Index Terms

Collaborative cloud-edge computation for personalized driving behavior modeling
1. Applied computing
  1. Operations research
    1. Transportation
2. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination

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cover image ACM Conferences

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

November 2019

455 pages

ISBN:9781450367332

DOI:10.1145/3318216

General Chairs:
Songqing Chen
George Mason University
,
Ryokichi Onishi
Toyota
,
Program Chairs:
Ganesh Ananthanarayanan
Microsoft Research
,
Qun Li
College of William & Mary

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Published: 07 November 2019

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SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing

November 7 - 9, 2019

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SEC '19 Paper Acceptance Rate 20 of 59 submissions, 34%;

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https://doi.org/10.1109/TSC.2024.3512949
Shan CGao RHan QLiu TYang ZZhang JXia Y(2025)KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration FrameworkIEEE Internet of Things Journal10.1109/JIOT.2024.346623112:2(2026-2042)Online publication date: 15-Jan-2025
https://doi.org/10.1109/JIOT.2024.3466231
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