Abstract
This paper presents a classification model to identify abnormal driving behavior on roads. Vehicle dynamics is considered. A LSTM recurrent neural network model-based is applied. The vehicle dynamics features are measured by smartphone inertial sensors. The real data obtained from the GPS, accelerometer, and gyroscope are used to classify the driving maneuvers. A conventional two-lane and a highway roads located in the Madrid Region, Spain, are used for this research. The results obtained with the proposed model are promising and suggest that this intelligent system can be used to warn drivers of a defective or distracted maneuver in real time, aiming at a safer and more comfortable driving.
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References
Meiring, G.A.M., Myburgh, H.C.: A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors 15(12), 30653–30682 (2015)
Weidner, W., Transchel, F.W., Weidner, R.: Telematic driving profile classification in car insurance pricing. Ann. Actuarial Sci. 11(2), 213–236 (2017)
Santos, M., López, V.: Fuzzy decision system for safety on roads. In: Lu, J., Jain, L.C., Zhang, G. (eds.) Handbook on Decision Making, pp. 171–187 (2012). Springer, Heidelberg. https://doi.org/10.1007/978-3-642-25755-1_9
Martín, S., Romana, M.G., Santos, M.: Fuzzy model of vehicle delay to determine the level of service of two-lane roads. Expert Syst. Appl. 54, 48–60 (2016)
Van, L.M., Martin, S., Trivedi, M.M.: Driver classification and driving style recognition using inertial sensors. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 1040–1045. IEEE, June 2013
Wu, C., Yu, D., Doherty, A., Zhang, T., Kust, L., Luo, G.: An investigation of perceived vehicle speed from a driver’s perspective. PLoS ONE 12(10), e0185347 (2017)
Lara-Benítez, P., Carranza-García, M., Riquelme, J.C.: An experimental review on deep learning architectures for time series forecasting. Int. J. Neural Syst. 31(03), 2130001 (2021)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489, June 2016
Moukafih, Y., Hafidi, H., Ghogho, M.: Aggressive driving detection using deep learning-based time series classification. In: 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–5. IEEE, July 2019
Matousek, M., Mohamed, E.Z., Kargl, F., Bösch, C.: Detecting anomalous driving behavior using neural networks. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 2229–2235. IEEE, June 2019
Shahverdy, M., Fathy, M., Berangi, R., Sabokrou, M.: Driver behavior detection and classification using deep convolutional neural networks. Expert Syst. Appl. 149, 113240 (2020)
Khodairy, M.A., Abosamra, G.: Driving behavior classification based on oversampled signals of smartphone embedded sensors using an optimized stacked-LSTM neural networks. IEEE Access 9, 4957–4972 (2021)
Barreno, F., Romana, M.G., Santos, M.: Fuzzy expert system for road type identification and risk assessment of conventional two‐lane roads. Expert Syst. e12837 (2021)
Rajamani, R.: Vehicle Dynamics and Control. Springer Science & Business Media (2011)
Transportation Officials. A Policy on Geometric Design of Highways and Streets, AASHTO (2011)
Bergasa, L.M., Almería, D., Almazán, J., Yebes, J.J., Arroyo, R.: Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors. In: 2014 IEEE Intelligent Vehicles symposium proceedings, pp. 240–245. IEEE, June 2014
Romera, E., Bergasa, L..M., Arroyo, R.: Need data for driver behaviour analysis? presenting the public UAH-DriveSet. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 387–392. IEEE, November 2016
Fitch, G.M., et al.: The impact of hand-held and hands-free cell phone use on driving performance and safety-critical event risk (No. DOT HS 811 757) (2013)
Chen, S., Zhang, S., Shang, J., Chen, B., Zheng, N.: Brain-inspired cognitive model with attention for self-driving cars. IEEE Trans. Cogn. Dev. Syst. 11(1), 13–25 (2017)
Wang, X., Xu, R., Zhang, S., Zhuang, Y., Wang, Y.: Driver distraction detection based on vehicle dynamics using naturalistic driving data. Transp. Res. Part Emerg. Technol. 136, 103561 (2022)
Barreno, F., Santos, M., Romana, M.G.: A novel adaptive vehicle speed recommender fuzzy system for autonomous vehicles on conventional two‐lane roads. Expert Syst. e13046 (2022)
Guevara, C., Santos, M.: Intelligent models for movement detection and physical evolution of patients with hip surgery. Logic J. IGPL 29(6), 874–888 (2021)
Maldonado, C.B.G., Penas, M.S., Lopez, M.V.L.: Negative selection and Knuth Morris Pratt algorithm for anomaly detection. IEEE Lat. Am. Trans. 14(3), 1473–1479 (2016)
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Barreno, F., Santos, M., Romana, M. (2023). Abnormal Driving Behavior Identification Based on Naturalistic Driving Data Using LSTM Recurrent Neural Networks. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_42
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DOI: https://doi.org/10.1007/978-3-031-18050-7_42
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