Driver Behavior Classification: A Novel Approach Using Auto-Encoders and Motif Extraction
Keywords:
Driver Behavior Detection, Time-Series Data Analysis, Motifs, Machine Learning, ADABoost.
Abstract
Driver’s behavior is expressed by the intentional or unintentional actions the driver performs while driving a motor vehicle. This behavior could be influenced by several factors such as driver’s fatigue, drowsiness, vehicle surroundings, or distraction state. Monitoring, analyzing and improving driver’s behavior can reduce traffic collisions and enhance road safety. Several approaches have been followed for the detection and identification of driver’s behavior. Conventional time-series analysis applies forecasting analysis methods for driver’s behavior detection, assuming that data are stationary and ergodic; otherwise data preprocessing is mandatory. Rule-based and deep learning approaches have succeeded to mine dynamical characteristics of driving time series data. However, they have some challenges, including the selection of efficient architectures and corresponding hyper-parameters, as well as slow training and limited labeled data. In this study, we propose a motif-based approach for categorizing driver behavior as normal or abnormal, using the UAH-DriveSet dataset. Our methodology entails the selection of relevant features, which are encoded using an auto-encoder model, followed by the conversion of the encoded data into an alphabet representation through quantization. Unique patterns of varying lengths are then extracted, and an AdaBoost classifier is utilized for behavior classification. Extracted motifs capture significant patterns, which enables to achieve higher accuracy in classification. The obtained results demonstrate the effectiveness of the proposed approach in accurately categorizing driver behavior, which can significantly contribute to the advancement of intelligent transportation systems and the enhancement of road safety.References
1. World Health Organization. Global status report on road safety, 2024.
2. Romera, E., Bergasa, L. M., & Arroyo, R. (2016). Need data for driver behaviour analysis? Presenting the public UAH-DriveSet. IEEE
Conference on Intelligent Transportation Systems, Proceedings, ITSC. https://doi.org/10.1109/ITSC.2016.7795584
3. Kabari, Ledisi, and Believe Nwamae. ”Principal component analysis (pca)-an effective tool in machine learning.” no. June (2019):
3-7.
4. Baldi, Pierre. ”Autoencoders, unsupervised learning, and deep architectures.” Proceedings of ICML workshop on unsupervised and
transfer learning. JMLR Workshop and Conference Proceedings, 2012.
5. Freund, Yoav, and Robert E. Schapire. ”Experiments with a new boosting algorithm.” icml. Vol. 96. 1996.
6. Saleh, K., Hossny, M., & Nahavandi, S. (2018). Driving behavior classification based on sensor data fusion using LSTM recurrent
neural networks. https://doi.org/10.1109/ITSC.2017.8317835
7. Sahoo, G.K., Das, S.K. & Singh, P. Two layered gated recurrent stacked long short-term memory networks for driver’s behavior
analysis. S¯adhan¯a 48, 75 (2023). https://doi.org/10.1007/s12046-023-02126-y
8. M. A. Khodairy and G. Abosamra, ”Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded
Sensors Using an Optimized Stacked-LSTM Neural Networks,” in IEEE Access, vol. 9, pp. 4957-4972, 2021, doi:
10.1109/ACCESS.2020.3048915.
9. Rabab Gamal, Mirvat Al-Qutt, and Said Ghoniemy, “Driver Behavior Detection in Time Series Decade”, International Journal of
Intelligent Computing and Information Sciences (IJICIS), Ain Shams University, 2023.
10. Rabab Gamal, Mirvat Al-Qutt, Mahmoud Fayez, Hebe Khaled, and Said Ghoniemy, “Impact of Feature Selection and Interactions on
Accuracy of Classical Machine Learning Algorithms: A Driver Behavior Case Study”.
11. Silva, M. I., & Henriques, R. (2020). Finding manoeuvre motifs in vehicle telematics. Accident Analysis & Prevention, 138, 105467.
https://doi.org/10.1016/j.aap.2020.105467
12. Silva, M. I., & Henriques, R. (2021). TripMD: Driving patterns investigation via motif analysis. Expert Systems with Applications,
184, 115527. https://doi.org/10.1016/j.eswa.2021.115527
13. Yoshiki Tanaka, Kazuhisa Iwamoto, and Kuniaki Uehara. Discovery of Time-Series Motif from Multi-Dimensional Data Based
on MDL Principle. Machine Learning, 58(2), 269–300, February 2005. ISSN 1573-0565. https://doi.org/10.1007/
s10994-005-5829-2
2. Romera, E., Bergasa, L. M., & Arroyo, R. (2016). Need data for driver behaviour analysis? Presenting the public UAH-DriveSet. IEEE
Conference on Intelligent Transportation Systems, Proceedings, ITSC. https://doi.org/10.1109/ITSC.2016.7795584
3. Kabari, Ledisi, and Believe Nwamae. ”Principal component analysis (pca)-an effective tool in machine learning.” no. June (2019):
3-7.
4. Baldi, Pierre. ”Autoencoders, unsupervised learning, and deep architectures.” Proceedings of ICML workshop on unsupervised and
transfer learning. JMLR Workshop and Conference Proceedings, 2012.
5. Freund, Yoav, and Robert E. Schapire. ”Experiments with a new boosting algorithm.” icml. Vol. 96. 1996.
6. Saleh, K., Hossny, M., & Nahavandi, S. (2018). Driving behavior classification based on sensor data fusion using LSTM recurrent
neural networks. https://doi.org/10.1109/ITSC.2017.8317835
7. Sahoo, G.K., Das, S.K. & Singh, P. Two layered gated recurrent stacked long short-term memory networks for driver’s behavior
analysis. S¯adhan¯a 48, 75 (2023). https://doi.org/10.1007/s12046-023-02126-y
8. M. A. Khodairy and G. Abosamra, ”Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded
Sensors Using an Optimized Stacked-LSTM Neural Networks,” in IEEE Access, vol. 9, pp. 4957-4972, 2021, doi:
10.1109/ACCESS.2020.3048915.
9. Rabab Gamal, Mirvat Al-Qutt, and Said Ghoniemy, “Driver Behavior Detection in Time Series Decade”, International Journal of
Intelligent Computing and Information Sciences (IJICIS), Ain Shams University, 2023.
10. Rabab Gamal, Mirvat Al-Qutt, Mahmoud Fayez, Hebe Khaled, and Said Ghoniemy, “Impact of Feature Selection and Interactions on
Accuracy of Classical Machine Learning Algorithms: A Driver Behavior Case Study”.
11. Silva, M. I., & Henriques, R. (2020). Finding manoeuvre motifs in vehicle telematics. Accident Analysis & Prevention, 138, 105467.
https://doi.org/10.1016/j.aap.2020.105467
12. Silva, M. I., & Henriques, R. (2021). TripMD: Driving patterns investigation via motif analysis. Expert Systems with Applications,
184, 115527. https://doi.org/10.1016/j.eswa.2021.115527
13. Yoshiki Tanaka, Kazuhisa Iwamoto, and Kuniaki Uehara. Discovery of Time-Series Motif from Multi-Dimensional Data Based
on MDL Principle. Machine Learning, 58(2), 269–300, February 2005. ISSN 1573-0565. https://doi.org/10.1007/
s10994-005-5829-2
Published
2025-02-15
How to Cite
Rabab Gamal, Mirvat Al-Qutt, Heba Khaled, Mahmoud Fayez, & Said Ghoniemy. (2025). Driver Behavior Classification: A Novel Approach Using Auto-Encoders and Motif Extraction. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2190
Issue
Section
Research Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
