A Comprehensive Framework For Traffic-Based Vehicle Rerouting and Driver Monitoring


Year : 2025 | Volume : 13 | Issue : 01 | Page : 32-47
    By

    Spriha Deshpande,

  1. Researcher, Spriha Santa Clara, CA, USA

Abstract

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Driver fatigue is a leading cause of road accidents, making real-time monitoring a critical aspect of transportation safety. This study presents an intelligent driver alertness detection system that integrates machine learning-based eye status prediction with Google Maps APIs for dynamic rerouting. Using convolutional neural networks (CNNs) that have been trained with data from the Munich Research Lab. (MRL) Eye Dataset, the system continuously monitors driver fatigue through image-based classification. Additionally, Google Maps’ real-time traffic data via Application Programmable Interface (API) enables dynamic route adjustments to optimize navigation. Implemented using Streamlit, the application provides an intuitive interface for real-time driver monitoring, traffic-based rerouting, and drowsiness alerts. This fusion of deep learning-based fatigue detection with intelligent route planning enhances both road safety and traffic management.

Keywords: Streamlit, driver monitoring, real-time simulation, traffic rerouting, driver alertness detection, google maps api, autonomous driving, machine learning, tensorflow, eye status prediction, dynamic route planning, traffic management, image processing, deep learning, smart transportation

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]

How to cite this article:
Spriha Deshpande. A Comprehensive Framework For Traffic-Based Vehicle Rerouting and Driver Monitoring. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(01):32-47.
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Spriha Deshpande. A Comprehensive Framework For Traffic-Based Vehicle Rerouting and Driver Monitoring. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(01):32-47. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=0


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References

  1. Capraro F, Milani S. Rendering-aware point cloud coding for mixed reality devices. In2019 IEEE International Conference on Image Processing (ICIP) 2019 Sep 22 (pp. 3706-3710). IEEE.
  2. Nikolskaia K, Bessonov V, Starkov A, Minbaleev A. Prototype of driver fatigue detection system using convolutional neural network. In2019 International Conference” Quality Management, Transport and Information Security, Information Technologies”(IT&QM&IS) 2019 Sep 23 (pp. 82-86). IEEE.
  3. Victoria DR, Mary DG. Driver drowsiness monitoring using convolutional neural networks. In2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) 2021 Feb 19 (pp. 1055-1059). IEEE.
  4. Panganai MA, Nyandoro LK, Zvarevashe K. Driver drowsiness detection using Convolutional Neural Networks-inspired features and Principal component analysis with K-Nearest Neighbors. In2022 1st Zimbabwe Conference of Information and Communication Technologies (ZCICT) 2022 Nov 9 (pp. 1-6). IEEE.
  5. Zhao Z, Zhou N, Zhang L, Yan H, Xu Y, Zhang Z. Driver fatigue detection based on convolutional neural networks using em‐CNN. Computational intelligence and neuroscience. 2020;2020(1):7251280.
  6. Zhou L, Li S, Wang Y. Fatigue Detection and Early Warning System for Drivers Based on Deep Learning. In2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA) 2023 Oct 27 (pp. 1348-1351). IEEE.
  7. Wijaya D, Stanley M, Wilman P, Lucky H, Chowanda A. A Fatigue Detection Model Based on Convolutional Neural Network. In2022 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) 2022 Nov 16 (pp. 166-171). IEEE.
  8. Ghazal M, Haeyeh YA, Abed A, Ghazal S. Embedded fatigue detection using convolutional neural networks with mobile integration. In2018 6th international conference on future internet of things and cloud workshops (ficloudw) 2018 Aug 6 (pp. 129-133). IEEE.
  9. Geocoding Service. Google for Developers. 2025 . Available from: https://developers.google.com/maps/documentation/javascript/geocoding
  10. Streamlit • A faster way to build and share data apps . Streamlit.io. 2024. Available from: https://streamlit.io/
  11. TAUIL Abd Elilah. Human eyes openclose. Kaggle.com. 2022. Available from: https://www.kaggle.com/datasets/tauilabdelilah/mrl-eye-dataset .
  12. S. Deshpande, “Traffic-Based Vehicle Rerouting and Driver Monitoring,” GitHub. [Online]. Available: https://github.com/SprihaDeshpande/Traffic-Based-Vehicle-Rerouting-and-Driver-Monitoring/
  13. parshva45. GitHub – parshva45/Vehicle-Monitoring-And-Routing-System: The Vehicle Monitoring And Routing System (VMARS) makes use of GPS to provide the exact location of the vehicle. It allows a parent to monitor the vehicle in real-time using a GPS-based device possessed by its driver. It is also capable of finding the shortest route to reach the destination passing through all the checkpoints which uses our proposed algorithm to find the same. This system also possesses the facility of speed monitoring and giving sound alerts to the driver if he over speeds his vehicle. Also, the system gives accident alerts to the vehicle admin via SMS. GitHub. 2018. Available from: https://github.com/parshva45/Vehicle-Monitoring-And-Routing-System
Regular Issue Subscription Original Research
Volume 13
Issue 01
Received 01/02/2025
Accepted 10/02/2025
Published 18/03/2025
Publication Time 45 Days
Total Visits: 18

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