Distracted Driver Detection Using AI
DOI:
https://doi.org/10.32628/IJSRST2512320Abstract
Driver distraction has emerged as one of the leading causes of road accidents in recent years, posing a significant threat to public safety. Tackling this critical issue has become a priority for researchers worldwide, leading to the development of various methodologies aimed at detecting and mitigating driver distraction. Among these, deep learning and machine learning techniques have become increasingly prominence due to their ability to analyze complex visual data and accurately classify driver behaviors.Traditional approaches to distracted driver detection typically rely on processing the entire image as input. However, this method can be inefficient and often introduces noise, as only processes the segmented ROI to classify driver behavior as distracted or attentive. This method enhances detection precision, reduces computational overhead, and is suitable for real-time applications. By integrating segmentation and classification, the model provides a robust and efficient solution for safer road environments through advanced deep learning techniques.
Downloads
References
World Health Organization, “Global status report on road safety2018.” [Online]. Available: https://www.who.int/publications/i/item/9789241565684. Accessed: August. 13, 2023.
Willens & Baez, “DISTRACTED DRIVING: A LEADING CAUSE OFCAR ACCIDENTS.” [Online].Available:https://www.willenslaw.com/distracted-driving-a-leading-cause-of-car-accidents-in-chicago/. Accessed: August. 13, 2023.
National Highway Trafic Safety Administrator, “What Is Distracted Driving?.” [Online]. Available: https://www.nhtsa.gov/risky-driving/distracted-driving. Accessed: June. 03, 2023.
Federal Communications Commission, “The Dangers of Distracted Driving.”Accessed: June. 03, 2023.[Online].Available:https://www.fcc.gov/consumers/guides/dangers-texting- while-driving.
H. V. Koay, J. H. Chuah, C.-O. Chow, and Y.-L.Chang“Detectingand recognizingdriverdistraction throughvarious data modality usingmachine learning: A review, recent advances, simplified framework and open challenges (2014–2021),”EngineeringApplicationsof ArtificialIntelligence, vol. 115, p. 105309, 2022.
M. Gjoreski, M. Z. Gams, M. Lu ˇ strek, P. Genc, J.-U. Garbas, and T. Has- ˇsan, “Machine learning and end-to-end deep learning for monitoringdriver distractions from physiological and visual signals,” IEEE access, vol. 8, pp. 70590–70603, 2020.
W. Li, J. Huang, G. Xie, F. Karray, and R. Li, “A survey on vision-based driver distraction analysis,” Journal of Systems Architecture, vol. 121, p. 102319, 2021.
S. Hao, Y. Zhou, and Y. Guo, “A brief survey on semantic segmentation with deep learning,” Neurocomputing, vol. 406, pp. 302–321, 2020.
A. Ziebinski, R. Cupek, D. Grzechca, and L. Chruszczyk, “Review of advanced driver assistance systems (adas),” in AIP Conference Proceedings, vol. 1906, AIP Publishing, 2017.
S. L. Gallahan, G. F. Golzar, A. P. Jain, A. E. Samay, T. J. Trerotola,J. G. Weisskopf, and N. Lau, “Detecting and mitigating driver distraction with motion capture technology: Distracted driving warning system,” in 2013 IEEE Systems and Information Engineering Design Symposium,pp. 76–81, IEEE, 2013.
T. E. Kalayci, E. G. Kalayci, G. Lechner, N. Neuhuber, M. Spitzer, E. Westermeier, and A. Stocker, “Triangulated investigation of trust in automated driving: Challenges and solution approaches for data integration,” Journal of Industrial Information Integration, vol. 21, p. 100186, 2021.
M. Q. Khan and S. Lee, “Gaze and eye tracking: Techniques and applications in adas,” Sensors, vol. 19, no. 24, p. 5540, 2019.
A. Kashevnik, R. Shchedrin, C. Kaiser, and A. Stocker, “Driver distraction detection methods: A literature review and framework,” IEEE Access, vol. 9, pp. 60063–60076, 2021.
S. Bouhsissin, N. Sael, and F. Benabbou, “Driver behavior classification:A systematic literature review,” IEEE Access, 2023.
X. Zhang, N. Zheng, F. Wang, and Y. He, “Visual recognition of driver hand-held cell phone use based on hidden crf,” in Proceedings of The IEEE Fifth International Conference on vehicular electronics and safety, pp. 248–251, IEEE, 2011.
C. H. Zhao, B. L. Zhang, X. Z. Zhang, S. Q. Zhao, and H. X. Li, “Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers,” Applications of Neural Computing, vol.22, pp. 175–184, 2013.
E. Ohn-Bar, S. Martin, and M. M. Trivedi, “Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies,” Journal of Electronic Imaging, vol. 22, no. 4, pp. 041119– 041119, 2013.
N. Das, E. Ohn-Bar, and M. M. Trivedi, “On performance evaluation of driver hand detection algorithms: Challenges, dataset, and metrics,” in 2015 conference on intelligent transportation systems, pp. 2953–2958, IEEE, 2015.
K. Seshadri, F. Juefei-Xu, D. K. Pal, M. Savvides, and C. P. Thor,“Driver cell phone usage detection on strategic highway research program (shrp2) face view videos Workshops, pp. 35–43, 2015.
T. Hoang Ngan Le, Y. Zheng, C. Zhu, K. Luu, and M. Savvides, “Multiple scale faster-rcnn approach to driver’s cell-phone usage and hands on steering wheel detection workshops, pp. 46–53, 2016.
Sunil Kumar, K “Effect of Silicon Carbide Particulates on Mechanical Behavior of Al5052 Alloy Metal Matrix Composites for Mining Applications” in Journal of Mines, Metals and Fuels Journal of Mines, Metals and Fuels, 72(8): 893-904; 2024. DOI: 10.18311/jmmf/2024/45421 Print ISSN : 0022-2755.
Sunil Kumar, K., & Srinivasamurthy, P. L. (2021). Tensile and hardness behavior of RRA treated aluminum 7075 alloy. Lecture Notes in Mechanical Engineering, 23, 563–575.
Sunil Kumar, K., & Srinivasa Murthy, P. L. (2019). Retrogression and re-aging of AL 7075 microstructure and corrosion behavior characterization. International Journal of Engineering and Advanced Technology, 9(1), 4377–4383.
Sunil Kumar, K., & Srinivasa Murthy, P. L. (2019). Effect of retrogression and re-aging on fatigue crack growth behavior of Al 7075 alloy. International Journal of Innovative Technology and Exploring Engineering, 8(10), 243–247.
Sunil Kumar, K., & Srinivasa Murthy, P. L. (2019). Study on tribological characterization of RRA treated aluminium 7075 alloy. Journal of Advanced Research in Dynamical and Control Systems, 11, 560–569.
Sunil Kumar, K. (2016). Development of standing wave thermo acoustic prime mover. International Journal of Research in Advent Technology, 4(4). E-ISSN: 2321-9637.
Sunil Kumar, K. (2016). Design and development of automated suspension to chassis damage. International Journal of Research in Advent Technology, 4(4). E-ISSN: 2561-9762.
Sunil Kumar, K. (2017). Effect of heat treatment on the corrosion behavior of high strength aluminium alloy. International Journal of Mechanical and Production Engineering, 5(10). ISSN: 2320-2092.
Sunil Kumar, K. (2017). A review on microstructure and corrosion behaviour of RRA treated 7075 Al alloy. International Journal of Scientific Research Engineering & Technology (IJSRET), 6(9). ISSN: 2278-0882.
Sunil Kumar, K. (n.d.). Design and analysis of a turning fixture for oil tank cover MGB. International Journal of Recent Trends in Engineering & Research. ISSN: 2455-1457.
Sunil Kumar, K. (2016). Pneumatic three axis modern trailer. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 4(5). ISSN: 2321-9653.
Sunil Kumar, K. (2024). Design and development of pneumatic sheet metal cutting machine. International Journal of Scientific Research in Mechanical and Materials Engineering (IJSRMME), 8(3). https://doi.org/10.32628/IJSRMME2483
Sunil Kumar, K. (2023). A new dimension in 3D printing. International Journal of Advanced Research in Science, Communication and Technology, 3(2). ISSN: 2581-9429.
Sunil Kumar, K. (2023). Design and development of dual extruder 3D printing. International Journal of Advanced Research in Science, Communication and Technology, 3(2). ISSN: 2581-9429.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
