Safety Embedded System Integration in Automotive Network CAN Bus to Prevent Road Accidents

Ismail Nasri; Abdelhafid Messaoudi; Kamal Kassmi; Mohammed Karrouchi; Hajar Snoussi

doi:10.15866/ireme.v15i8.21175

Safety Embedded System Integration in Automotive Network CAN Bus to Prevent Road Accidents

Ismail Nasri^(1*), Abdelhafid Messaoudi⁽²⁾, Kamal Kassmi⁽³⁾, Mohammed Karrouchi⁽⁴⁾, Hajar Snoussi⁽⁵⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/ireme.v15i8.21175

Abstract

In recent years, safety driving has attracted growing interest from the scientific and the industrial communities. This interest is meant to reduce the road accidents caused by driver behavior, fatigue, drugs, etc. Drowsy driving is one of the first factors of road accidents; it compromises driving ability by reducing alertness and attentiveness, delaying reaction times, etc. In this paper, a solution to prevent road accidents based on driver drowsiness detection that can be connected to the other Electronic Control Units (ECUs) in the vehicle network through the Controller Area Network Protocol (CAN) Bus to act on the functionalities of the vehicle when the driver starts feeling (yawning) or become drowsy has been proposed. The proposed solution has achieved an average accuracy of 99.10%. The simulation results confirm that the proposed system can efficiently detect driver drowsiness and warn the driver before anything undesired happens in order to ensure a sufficient and acceptable level of safety. A comparison with recently published works in this field is also provided.
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Keywords

Safety Driving; Drowsiness Detection; Yawn Detection; Road Accidents; CAN Bus; Automotive Network

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References

"Drowsy Driving | NHTSA." (accessed May 13, 2021) https://www.nhtsa.gov/risky-driving/drowsy-driving.

B. C. Tefft, Prevalence of motor vehicle crashes involving drowsy drivers, United States, 1999-2008, Accident Analysis & Prevention, vol. 45, pp. 180-186, Mar. 2012.
https://doi.org/10.1016/j.aap.2011.05.028

C. Berka et al., EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks, Aviation, Space, and Environmental Medicine, vol. 78, no. 5, pp. B231-B244, May 2007.

H.-S. Shin, S.-J. Jung, J.-J. Kim, and W.-Y. Chung, Real time car driver's condition monitoring system, in 2010 IEEE Sensors, Nov. 2010, pp. 951-954.
https://doi.org/10.1109/ICSENS.2010.5690904

L. Chen, Y. Zhao, J. Zhang, and J. Zou, Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning, Expert Systems with Applications, vol. 42, no. 21, pp. 7344-7355, Nov. 2015.
https://doi.org/10.1016/j.eswa.2015.05.028

T. Ouyang and H.-T. Lu, Vigilance Analysis Based on Continuous Wavelet Transform of EEG Signals, in 2010 International Conference on Biomedical Engineering and Computer Science, Apr. 2010, pp. 1-4.
https://doi.org/10.1109/ICBECS.2010.5462289

B. F. Momin, Current Status and Future Research Directions in Monitoring Vigilance of Individual or Mass Audience in Monotonous Working Environment, IJSC, vol. 3, no. 2, pp. 45-53, May 2012.
https://doi.org/10.5121/ijsc.2012.3204

I. Nasri, M. Karrouchi, H. Snoussi, K. Kassmi, and A. Messaoudi, Detection and Prediction of Driver Drowsiness for the Prevention of Road Accidents Using Deep Neural Networks Techniques, in WITS 2020, Singapore, 2022, pp. 57-64.
https://doi.org/10.1007/978-981-33-6893-4_6

U. Trutschel, B. Sirois, D. Sommer, M. Golz, and D. Edwards, PERCLOS: An Alertness Measure of the Past, in Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : driving assessment 2011, Olympic Valley-Lake Tahoe, California, USA>, 2011, pp. 172-179.
https://doi.org/10.17077/drivingassessment.1394

M. Golz, D. Sommer, M. Chen, U. Trutschel, and D. Mandic, Feature Fusion for the Detection of Microsleep Events, J VLSI Sign Process Syst Sign Im, vol. 49, no. 2, pp. 329-342, Nov. 2007.
https://doi.org/10.1007/s11265-007-0083-4

S. Masood, A. Rai, A. Aggarwal, M. N. Doja, and M. Ahmad, Detecting distraction of drivers using Convolutional Neural Network, Pattern Recognition Letters, vol. 139, pp. 79-85, Nov. 2020.
https://doi.org/10.1016/j.patrec.2017.12.023

F. Omerustaoglu, C. O. Sakar, and G. Kar, Distracted driver detection by combining in-vehicle and image data using deep learning, Applied Soft Computing, vol. 96, p. 106657, Nov. 2020.
https://doi.org/10.1016/j.asoc.2020.106657

S. Albawi, T. A. Mohammed, and S. Al-Zawi, Understanding of a convolutional neural network, in 2017 International Conference on Engineering and Technology (ICET), Aug. 2017, pp. 1-6.
https://doi.org/10.1109/ICEngTechnol.2017.8308186

E. Ericsson, Variability in urban driving patterns, Transportation Research Part D: Transport and Environment, vol. 5, no. 5, pp. 337-354, Sep. 2000.
https://doi.org/10.1016/S1361-9209(00)00003-1

A. Mashko, Review of approaches to the problem of driver fatigue and drowsiness, in 2015 Smart Cities Symposium Prague (SCSP), Jun. 2015, pp. 1-5.
https://doi.org/10.1109/SCSP.2015.7181569

M. Shahverdy, M. Fathy, R. Berangi, and M. Sabokrou, Driver behavior detection and classification using deep convolutional neural networks, Expert Systems with Applications, vol. 149, p. 113240, Jul. 2020.
https://doi.org/10.1016/j.eswa.2020.113240

T. Dittel and H.-J. Aryus, How to 'Survive' a Safety Case According to ISO 26262, in Computer Safety, Reliability, and Security, Berlin, Heidelberg, 2010, pp. 97-111.
https://doi.org/10.1007/978-3-642-15651-9_8

S.-H. Jeon, J.-H. Cho, Y. Jung, S. Park, and T.-M. Han, Automotive hardware development according to ISO 26262, in 13th International Conference on Advanced Communication Technology (ICACT2011), Feb. 2011, pp. 588-592.

R. Jabbar, K. Al-Khalifa, M. Kharbeche, W. Alhajyaseen, M. Jafari, and S. Jiang, Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques, Procedia Computer Science, vol. 130, pp. 400-407, Jan. 2018.
https://doi.org/10.1016/j.procs.2018.04.060

D. E. King, Dlib-ml: A Machine Learning Toolkit, p. 4.

J. A. K. Suykens and J. Vandewalle, Training multilayer perceptron classifiers based on a modified support vector method, IEEE Transactions on Neural Networks, vol. 10, no. 4, pp. 907-911, Jul. 1999.
https://doi.org/10.1109/72.774254

A. Karkouch, H. Mousannif, and H. Al Moatassime, CADS: A Connected Assistant for Driving Safe, Procedia Computer Science, vol. 127, pp. 353-359, Jan. 2018.
https://doi.org/10.1016/j.procs.2018.01.132

R. Oyini Mbouna, S. G. Kong, and M.-G. Chun, Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1462-1469, Sep. 2013.
https://doi.org/10.1109/TITS.2013.2262098

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, Support vector machines, IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18-28, Jul. 1998.
https://doi.org/10.1109/5254.708428

S. S. Kulkarni, A. D. Harale, and A. V. Thakur, Image processing for driver's safety and vehicle control using raspberry Pi and webcam, in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Sep. 2017, pp. 1288-1291.
https://doi.org/10.1109/ICPCSI.2017.8391917

C. B. S. Maior, M. J. das C. Moura, J. M. M. Santana, and I. D. Lins, Real-time classification for autonomous drowsiness detection using eye aspect ratio, Expert Systems with Applications, vol. 158, p. 113505, Nov. 2020.
https://doi.org/10.1016/j.eswa.2020.113505

L. Breiman, Random Forests, Machine Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001.
https://doi.org/10.1023/A:1010933404324

Soukupova, Tereza, and Jan Cech. Eye blink detection using facial landmarks. 21st computer vision winter workshop, Rimske Toplice, Slovenia. 2016.

Ghoddoosian, Reza, Marnim Galib, and Vassilis Athitsos. A realistic dataset and baseline temporal model for early drowsiness detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019.
https://doi.org/10.1109/CVPRW.2019.00027

T. Åkerstedt and M. Gillberg, Subjective and Objective Sleepiness in the Active Individual, International Journal of Neuroscience, vol. 52, no. 1-2, pp. 29-37, Jan. 1990.
https://doi.org/10.3109/00207459008994241

C. Goutte and E. Gaussier, A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation, in Advances in Information Retrieval, Berlin, Heidelberg, 2005, pp. 345-359.
https://doi.org/10.1007/978-3-540-31865-1_25

Marrugo Cardenas, N., Amaya Hurtado, D., Ramos Sandoval, O., Comparison of Multi-Class Methods of Features Extraction and Classification to Recognize EEGs Related with the Imagination of Two Vowels, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 398-405.
https://doi.org/10.15866/irecap.v8i5.12709

Hendel, M., Benyettou, A., Hendel, F., Fusion of Direct Probabilistic Multi-Class Support Vector Machines to Enhance Mental Tasks Recognition Performance in BCI Systems, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 430-438.
https://doi.org/10.15866/irecap.v8i5.14068

Moloi, K., Jordaan, J., Hamam, Y., The Development of a High Impedance Fault Diagnostic Scheme on Power Distribution Network, (2020) International Review of Electrical Engineering (IREE), 15 (1), pp. 69-79.
https://doi.org/10.15866/iree.v15i1.17074

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