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PBE: Driver Behavior Assessment Beyond Trajectory Profiling

  • Bing He
  • Xiaolin Chen
  • Dian ZhangEmail author
  • Siyuan Liu
  • Dawei Han
  • Lionel M. Ni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

Nowadays, the increasing car accidents ask for the better driver behavior analysis and risk assessment for travel safety, auto insurance pricing and smart city applications. Traditional approaches largely use GPS data to assess drivers. However, it is difficult to fine-grained assess the time-varying driving behaviors. In this paper, we employ the increasingly popular On-Board Diagnostic (OBD) equipment, which measures semantic-rich vehicle information, to extract detailed trajectory and behavior data for analysis. We propose PBE system, which consists of Trajectory Profiling Model (PM), Driver Behavior Model (BM) and Risk Evaluation Model (EM). PM profiles trajectories for reminding drivers of danger in real-time. The labeled trajectories can be utilized to boost the training of BM and EM for driver risk assessment when data is incomplete. BM evaluates the driving risk using fine-grained driving behaviors on a trajectory level. Its output incorporated with the time-varying pattern, is combined with the driver-level demographic information for the final driver risk assessment in EM. Meanwhile, the whole PBE system also considers the real-world cost-sensitive application scenarios. Extensive experiments on the real-world dataset demonstrate that the performance of PBE in risk assessment outperforms the traditional systems by at least 21%.

Keywords

Driver behavior analysis On-Board Diagnostic (OBD) 

Notes

Acknowledgements

This research was supported by Shenzhen Peacock Talent Grant 827-000175, Guangdong Pre-national Project 2014GKXM054, the University of Macau Start-up Research Grant (SRG2015-00050-FST) and Research & Development Grant for Chair Professor (CPG2015-00017-FST), and Natural Science Foundation of China: 61572488 and 61673241.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bing He
    • 2
  • Xiaolin Chen
    • 1
  • Dian Zhang
    • 1
    Email author
  • Siyuan Liu
    • 3
  • Dawei Han
    • 4
  • Lionel M. Ni
    • 2
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.Department of Computer and Information ScienceUniversity of MacauMacau SARChina
  3. 3.Smeal College of BusinessPennsylvania State UniversityState CollegeUSA
  4. 4.Auto Insurance DepartmentChina Pacific Insurance CompanyShenzhenChina

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