Inaccurate Prediction Is Not Always Bad: Open-World Driver Recognition via Error Analysis

Jianfeng Li, Kaifa Zhao, Yajuan Tang, Xiapu Luo, Xiaobo Ma

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Driver identification is of fundamental importance in many vehicle-related applications, such as fleet monitoring and anti-theft system. The vast majority of existing methods work under the closed-world assumption, which may be unrealistic in practice. In this paper, we consider a more practical but challenging scenario, i.e., open-world driver recognition, and propose a systematic method dubbed DRIVERPRINT. To recognize the driver of interest, DRIVERPRINT takes advantage of the behavioral predictability of the driver himself, thereby no need to collect data from other drivers for model training. Specifically, DRIVERPRINT predicts the behavior-related traveling speed with a driver-specific predictor, compares the prediction error with a pre-trained error model and finally recognizes drivers via error analysis. Besides open-world setting, our method is also compatible with closed-world driver classification. Real-world experiments demonstrate our method achieves reasonable accuracy. The average F1-score for open-world driver recognition is up to 0.91, while that for closed-world driver classification is up to 0.973.

Original languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189642
Publication statusPublished - Apr 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: 25 Apr 202128 Apr 2021

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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