Driver Distraction Detection Based on Cloud Computing Architecture and Lightweight Neural Network

Xueda Huang, Shaowen Wang, Guanqiu Qi (), Zhiqin Zhu, Yuanyuan Li, Linhong Shuai, Bin Wen, Shiyao Chen and Xin Huang
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Xueda Huang: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 40065, China
Shaowen Wang: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 40065, China
Guanqiu Qi: Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA
Zhiqin Zhu: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 40065, China
Yuanyuan Li: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 40065, China
Linhong Shuai: Intelligent Interaction R&D Department, Chongqing LiLong Zhongbao Intelligent Technology Co., Chongqing 40065, China
Bin Wen: Chongqing Dima Industrial Co., Ltd., Chongqing 40065, China
Shiyao Chen: Chongqing Dima Industrial Co., Ltd., Chongqing 40065, China
Xin Huang: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 40065, China

Mathematics, 2023, vol. 11, issue 23, 1-21

Abstract: Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved.

Keywords: driving distraction behavior detection; cloud–fog computing architecture; service computing; scalable networks; lightweighting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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