SmartDrive: An Android-Based Machine Learning Application: Vehicle Diagnostics and Driver Safety
Keywords:
ML, Android, Vehicle, Application, Diagnostics, Safety, Learning, DriverAbstract
SmartDrive makes use of Android smartphones, OBD-II plugs and on-device artificial intelligence to improve diagnostics and keep drivers safe. As a result, it lets users predict when equipment will fail and find any errors, supports driver monitoring and offers an inexpensive, secure and flexible option for proper vehicle management and safer travel.
References
Iskandar, K., Tambayong, A., Mulya, M. R. F., Elfanlie, S. C., & Herlina, M. G. (2023). Mobile-Based Car Diagnostic Application using Onboard Diagnostic-II Scanner. ComTech Computer Mathematics and Engineering Applications, 14(2), 129–141. https://doi.org/10.21512/comtech.v14i2.9138
Yen, M., Tian, S., Lin, Y., Yang, C., & Chen, C. (2021). Combining a Universal OBD-II Module with Deep Learning to Develop an Eco-Driving Analysis System. Applied Sciences, 11(10), 4481. https://doi.org/10.3390/app11104481
Hou, J., Zhang, B., Zhong, Y., & He, W. (2025). Research progress of dangerous driving behavior recognition methods based on deep learning. World Electric Vehicle Journal, 16(2), 62. https://doi.org/10.3390/wevj16020062
Jabbar, R., Shinoy, M., Kharbeche, M., Al-Khalifa, K., Krichen, M., & Barkaoui, K. (2020). Driver Drowsiness detection model using convolutional neural networks techniques for Android Application. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2002.03728
Hernandez, R. M., Manzanal, J., Guillo, J. R., Garcia, S. M., & Babol, M. (2023). DRIVEMATE: Empowering Safe Driving Through Real-Time Traffic Sign Detection and Speech Feedback on Mobile Devices Using YOLOv5 Algorithm and TensorFlow Lite. SIET ’23: Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology, 181–189. https://doi.org/10.1145/3626641.3627610
Brahim, S. B., Ghazzai, H., Besbes, H., & Massoud, Y. (2022). A machine learning smartphone-based sensing for driver behavior classification. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2202.01893
Jabbar, R., Al-Khalifa, K., Kharbeche, M., Alhajyaseen, W., Jafari, M., & Jiang, S. (2018). Real-time driver drowsiness detection for Android application using deep neural networks techniques. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1811.01627
Mahale, Y., Kolhar, S., & More, A. S. (2025). A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: technologies, challenges and future research directions. Deleted Journal, 7(4). https://doi.org/10.1007/s42452-025-06681-3
Shukla, S. K., Jain, P., Gupta, A., Gupta, V. K., Jindal, H., & Wadhawan, V. (2025). Early drowsiness detection of drivers using machine learning algorithm to prevent road accidents. AIP Conference Proceedings, 3253, 030016. https://doi.org/10.1063/5.0249396
Lokman, E. H. B. J. M., Goh, V. T., Yap, T. T. V., & Ng, H. (2022). Driving event recognition using machine learning and smartphones. F1000Research, 11, 57. https://doi.org/10.12688/f1000research.73134.2
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Copyright (c) 2025 Rama Krishna Velpula (Author)

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