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.
|Title of host publication
|Information Security Applications - 22nd International Conference, WISA 2021, Revised Selected Papers
|Springer Science and Business Media Deutschland GmbH
|Number of pages
|Published - 2021
|22nd World Conference on Information Security Application, WISA 2021 - Jeju, Korea, Republic of
Duration: 2021 Aug 11 → 2021 Aug 13
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|22nd World Conference on Information Security Application, WISA 2021
|Korea, Republic of
|21/8/11 → 21/8/13
Bibliographical noteFunding 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).
© 2021, Springer Nature Switzerland AG.
- Driver behavior profiling
- Recurrent neural networks
- Unsupervised learning
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science