research-article

Driver Identification Using Optimized Deep Learning Model in Smart Transportation

Authors:

Chandrasekar Ravi,

G. Thippa Reddy,

Mamoun AlazabAuthors Info & Claims

ACM Transactions on Internet Technology, Volume 22, Issue 4

Article No.: 84, Pages 1 - 17

https://doi.org/10.1145/3412353

Published: 14 November 2022 Publication History

Abstract

The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure, and comfortable for the people. Although vehicles have been automated up to a certain extent, it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified: 1) face recognition of the driver, and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short-Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score, and ROC AUC curve are considered to evaluate the performance of the model.

References

[1]

Kui Ren, Qian Wang, Cong Wang, Zhan Qin, and Xiaodong Lin. 2019. The security of autonomous driving: Threats, defenses, and future directions. Proceedings of the IEEE (2019). DOI:

[2]

Abdellah El Mekki, Afaf Bouhoute, and Ismail Berrada. 2019. Improving driver identification for the next-generation of in-vehicle software systems. IEEE Transactions on Vehicular Technology 68, 8 (2019): 7406–7415. DOI:

[3]

Na Lin, Changfu Zong, Masayoshi Tomizuka, Pan Song, Zexing Zhang, and Gang Li. 2014. An overview on study of identification of driver behavior characteristics for automotive control. Mathematical Problems in Engineering (2014). DOI:

[4]

William Grant Hatcher and Wei Yu. 2018. A survey of deep learning: Platforms, applications and emerging research trends. IEEE Access 6 (2018): 24411–24432. DOI:

[5]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (November 1997), 1735–1780. DOI:

Digital Library

[6]

Zahid Mahmood, Ossama Haneef, Nazeer Muhammad, and Shahid Khattak. 2018. Towards a fully automated car parking system. IET Intelligent Transport Systems 13, 2 (2018), 293–302. DOI:

[7]

Prabal Deep Das and Sharmila Sengupta. 2017. Implementing a next generation system to provide protection to vehicles from thefts and accidents. In 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT). IEEE, 2017. 1–6. DOI:

[8]

Byung Il Kwak, JiYoung Woo, and Huy Kang Kim. 2016. Know your master: Driver profiling-based anti-theft method. In 2016 14th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2016, 211–218. DOI:

[9]

Jun Zhang, ZhongCheng Wu, Fang Li, Chengjun Xie, Tingting Ren, Jie Chen, and Liu Liu. 2019. A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data. Sensors 19, 6 (2019), 1356. DOI:

[10]

Khaled Saleh, Mohammed Hossny, and Saeid Nahavandi. 2017. Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. 1–6. DOI:

[11]

Qi Wang, Jia Wan, and Xuelong Li. 2018. Robust hierarchical deep learning for vehicular management. IEEE Transactions on Vehicular Technology 68, 5 (2018). 4148–4156. DOI:

[12]

Wenhao Huang, Guojie Song, Haikun Hong, and Kunqing Xie. 2014. Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems 15, 5 (2014): 2191–2201. DOI:

[13]

Matthew Veres and Medhat Moussa. 2019. Deep learning for intelligent transportation systems: A survey of emerging trends. IEEE Transactions on Intelligent Transportation Systems (2019). DOI:

[14]

Sachin S. Talathi. 2015. Hyper-parameter optimization of deep convolutional networks for object recognition. In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. 3982–3986. DOI:

Digital Library

[15]

Bahareh Nakisa, Mohammad Naim Rastgoo, Andry Rakotonirainy, Frederic Maire, and Vinod Chandran. 2018. Long short term memory hyperparameter optimization for a neural network based emotion recognition framework. IEEE Access 6 (2018), 49325–49338. DOI:

[16]

Hyejung Chung and Kyung-shik Shin. 2018. Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability 10, 10 (2018): 3765. DOI:

[17]

AbdElRahman ElSaid, Fatima El Jamiy, James Higgins, Brandon Wild, and Travis Desell. 2018. Using ant colony optimization to optimize long short-term memory recurrent neural networks. In Proceedings of the Genetic and Evolutionary Computation Conference. 2018, 13–20. DOI:

Digital Library

[18]

Haruna Chiroma, Abdulsalam Ya'U. Gital, Nadim Rana, M. Abdulhamid Shafi'i, Amina N. Muhammad, Aishatu Yahaya Umar, and Adamu I. Abubakar. 2019. Nature inspired meta-heuristic algorithms for deep learning: Recent progress and novel perspective. In Science and Information Conference. Springer, Cham, 2019, 59–70. DOI:

[19]

Alireza Askarzadeh. 2016. A novel metaheuristic method for solving constrained engineering optimization problems: Crow Search Algorithm. Computers & Structures 169 (2016), 1–12. DOI:

Digital Library

[20]

Fabio Martinelli, Francesco Mercaldo, Albina Orlando, Vittoria Nardone, Antonella Santone, and Arun Kumar Sangaiah. 2018. Human behavior characterization for driving style recognition in vehicle system. Computers & Electrical Engineering (2018). DOI:

[21]

Rakesh Rana and Richa Singhal. 2015. Chi-square test and its application in hypothesis testing. Journal of the Practice of Cardiovascular Sciences 1, 1 (2015), 69. DOI:

[22]

S. Visalakshi and V. Radha. 2014. A literature review of feature selection techniques and applications: Review of feature selection in data mining. In 2014 IEEE International Conference on Computational Intelligence and Computing Research. IEEE, 2014. 1–6. DOI:

[23]

Dejun Zhang, Lu Zou, Xionghui Zhou, and Fazhi He. 2018. Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access 6 (2018), 28936–28944. DOI:

[24]

Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, and Dong In Kim. 2019. Applications of deep reinforcement learning in communications and networking: A survey. IEEE Communications Surveys & Tutorials 21, 4 (2019), 3133–3174. DOI:

Digital Library

[25]

Qi Chen, Wei Wang, Fangyu Wu, Suparna De, Ruili Wang, Bailing Zhang, and Xin Huang. 2019. A survey on an emerging area: Deep learning for smart city data. IEEE Transactions on Emerging Topics in Computational Intelligence 3, 5 (2019), 392–410. DOI:

[26]

Jin-Young Kim and Sung-Bae Cho. 2019. Evolutionary optimization of hyperparameters in deep learning models. In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. 831–837. DOI:

Digital Library

[27]

Haruna Chiroma, Abdulsalam Ya'U. Gital, Nadim Rana, M. Abdulhamid Shafi'i, Amina N. Muhammad, Aishatu Yahaya Umar, and Adamu I. Abubakar. 2019. Nature inspired meta-heuristic algorithms for deep learning: Recent progress and novel perspective. In Science and Information Conference. Springer, Cham, 2019, 59–70. DOI:

[28]

Chigozie Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall. 2018. Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378 (2018). DOI:https://arxiv.org/abs/1811.03378v1

[29]

Yoav Goldberg. 2016. A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research 57 (2016), 345–420. DOI:https://arxiv.org/abs/1510.00726

Digital Library

[30]

Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, and George E. Dahl. 2019. On empirical comparisons of optimizers for deep learning. arXiv preprint arXiv:1910.05446 (2019).

[31]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[32]

Mohammad Hossin and M. N. Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 5, 2 (2015), 1. DOI:

[33]

T. R. Gadekallu, N. Khare, S. Bhattacharya, S. Singh, P. K. Reddy Maddikunta, I. H. Ra, and M. Alazab. 2020. Early detection of diabetic retinopathy using PCA-Firefly based deep learning model. Electronics 9, 2 (2020), 274.

[34]

S. Bhattacharya, R. Kaluri, S. Singh, M. Alazab, and U. Tariq. 2020. A novel PCA-Firefly based XGBoost classification model for intrusion detection in networks using GPU. Electronics 9, 2 (2020), 219.

[35]

G. T. Reddy, M. P. K. Reddy, K. Lakshmanna, R. Kaluri, D. S. Rajput, G. Srivastava, and T. Baker. 2020. Analysis of dimensionality reduction techniques on big data. IEEE Access, 8, 54776–54788.

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Biggs TLanigan TRuddell DGallegos EDaily J(2024)Modeling a Heavy-duty Vehicle Data Collection Process for Authenticating Driver Identity and Analyzing Driver Behavior Under Duress2024 19th Annual System of Systems Engineering Conference (SoSE)10.1109/SOSE62659.2024.10620958(256-263)Online publication date: 23-Jun-2024
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Index Terms

Driver Identification Using Optimized Deep Learning Model in Smart Transportation
1. Security and privacy
  1. Network security

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 22, Issue 4

November 2022

642 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3561988

Editor:
Ling Liu
TU Wien, Austria

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2022

Online AM: 12 February 2022

Accepted: 23 July 2020

Revised: 08 June 2020

Received: 27 April 2020

Published in TOIT Volume 22, Issue 4

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https://doi.org/10.3390/software3010003
Biggs TLanigan TRuddell DGallegos EDaily J(2024)Modeling a Heavy-duty Vehicle Data Collection Process for Authenticating Driver Identity and Analyzing Driver Behavior Under Duress2024 19th Annual System of Systems Engineering Conference (SoSE)10.1109/SOSE62659.2024.10620958(256-263)Online publication date: 23-Jun-2024
https://doi.org/10.1109/SOSE62659.2024.10620958
Krishnan DSrinivasan DBalasubramanian VPrabagaranae NMertens JCafiso SGalluccio LMorabito GPappalardo G(2024)Driving Style Profiling Using Deep Autoencoders for Safety Applications in Urban and Highway Scenarios2024 IEEE International Symposium on Measurements & Networking (M&N)10.1109/MN60932.2024.10615387(1-6)Online publication date: 2-Jul-2024
https://doi.org/10.1109/MN60932.2024.10615387
Alsubai SAlqahtani AAlanazi ABhatia M(2024)Digital-Twin-Inspired IoT-Assisted Intelligent Performance Analysis Framework for Electric VehiclesIEEE Internet of Things Journal10.1109/JIOT.2024.336774511:10(18880-18887)Online publication date: 15-May-2024
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Ebert JXiong RPatel AAbdel-Rahman DMcDonald CKit Delgado M(2024)Validation of a smartphone telematics algorithm for classifying driver tripsTransportation Research Interdisciplinary Perspectives10.1016/j.trip.2024.10110925(101109)Online publication date: May-2024
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Suk HLee YKim TKim S(2024)Addressing uncertainty challenges for autonomous driving in real-world environmentsArtificial Intelligence and Machine Learning for Open-world Novelty10.1016/bs.adcom.2023.06.004(317-361)Online publication date: 2024
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Murugan SSivakumar PKavitha CHarichandran ALai W(2023)An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine LearningSensors10.3390/s2306294423:6(2944)Online publication date: 8-Mar-2023
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