OnlineDC: Leveraging Temporal Driving Behavior to Facilitate Driver Classification

Hashim Abu-Gellban, Yu Zhuang, Long Nguyen, Fang Jin, Zhenkai Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Driver classification is used recently for vehicle anti-burglary and fake driver accounts based on driving behavior. Anti-burglary is a challenging problem as it leans on external devices to defend against vehicle theft. Several researchers analyzed the driving behavior to identify drivers, but they faced several challenges to produce a stable model for the cold start problem and for medium-long sequences. In addition, some approaches had an unpleasant performance when the action space increased (> 2 drivers). In this paper, we propose a novel approach named OnlineDC (Online Driver Classification), which leverages temporal driving behavior to identify a human subject behind the wheel. Our method utilizes the Gated Recurrent Unit (GRU) and the ResNet with the Squeeze-Excite blocks (SE) to analyze the long-short term patterns of driving behaviors. Moreover, we fostered the performance by building and applying the Feature Generation (FG) algorithm to extract spectral, temporal, and statistical features from the sensing data of vehicles. We conducted extensive experiments to show how our approach outperformed state-of-the-art baseline methods. The results also showed that our solution could resolve the cold-start problem for short patterns.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2857-2866
Number of pages10
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/15/2112/18/21

Keywords

  • Cybersecurity
  • Deep Learning
  • Driver Classification
  • Internet of Things
  • Neural Network

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