Unsupervised Driver Behavior Profiling Leveraging Recurrent Neural Networks

Young Ah Choi, Kyung Ho Park, Eunji Park, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


In the era of intelligent transportation, driver behavior profiling has become a beneficial technology as it provides knowledge regarding the driver’s aggressiveness. Previous approaches achieved promising driver behavior profiling performance through establishing statistical heuristics rules or supervised learning-based models. Still, there exist limits that the practitioner should prepare a labeled dataset, and prior approaches could not classify aggressive behaviors which are not known a priori. In pursuit of improving the aforementioned drawbacks, we propose a novel approach to driver behavior profiling leveraging an unsupervised learning paradigm. First, we cast the driver behavior profiling problem as anomaly detection. Second, we established recurrent neural networks that predict the next feature vector given a sequence of feature vectors. We trained the model with normal driver data only. As a result, our model yields high regression error given a sequence of aggressive driver behavior and low error given at a sequence of normal driver behavior. We figured this difference of error between normal and aggressive driver behavior can be an adequate flag for driver behavior profiling and accomplished a precise performance in experiments. Lastly, we further analyzed the optimal level of sequence length for identifying each aggressive driver behavior. We expect the proposed approach to be a useful baseline for unsupervised driver behavior profiling and contribute to the efficient, intelligent transportation ecosystem.

Original languageEnglish
Title of host publicationInformation Security Applications - 22nd International Conference, WISA 2021, Revised Selected Papers
EditorsHyoungshick Kim
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030894313
Publication statusPublished - 2021
Event22nd World Conference on Information Security Application, WISA 2021 - Jeju, Korea, Republic of
Duration: 2021 Aug 112021 Aug 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd World Conference on Information Security Application, WISA 2021
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-00624, Development of Intelligence Cyber Attack and Defense Analysis Framework for Increasing Security Level of C-ITS).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Driver behavior profiling
  • Recurrent neural networks
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Unsupervised Driver Behavior Profiling Leveraging Recurrent Neural Networks'. Together they form a unique fingerprint.

Cite this