A Machine Learning Approach to Real Time Vehicle Safety System

Shivani Jagdish Pawar*, Rameez Shamalik**, Amruta Ajay Pol***, Rajashri Dattatray Rajage****
*-**** Department of Electronics and Telecommunication, Bharati Vidyapeeth's College of Engineering for Women, Pune, Maharashtra, India.
Periodicity:January - June'2025

Abstract

Vehicle security could be a developing concern in today's world, with increasing cases of robbery and unauthorized access. This paper presents an AI-powered vehicle security framework that guarantees access to only authorized and enlisted users to start the vehicle. The framework utilizes fake insights procedures for client confirmation, avoiding unauthorized get-in and activating prompt e-mail alarms to the proprietor in case of suspicious movement. All authorized client information, counting get to times and movement logs, is put away safely in a backend framework, open through a web-based dashboard for real-time observation. It uses Raspberry Pi as hardware and machine learning for computing and computation to improve security and decision-making. The solution is to predict unauthorized access and generate alarms in real time, and implement a reliable solution for modern security and challenges, and data of authorized vehicle users are stored for real-time monitoring.

Keywords

Machine Learning, Computer Vision, Open CV, Datasets, Face Recognition, Predictive Modelling.

How to Cite this Article?

Pawar, S. J., Shamalik, R., Pol, A. A., and Rajage, R. D. (2025). A Machine Learning Approach to Real Time Vehicle Safety System. International Journal of Data Mining Techniques and Applications, 14(1), 1-8.

References

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[8]. Shukla, A., Jain, A., Chauhan, P. S., & Singh, R. (2024). IoT ecosystem-level security auditing, analysis, and recovery. In Secure Communication in Internet of Things (pp. 228-239). CRC Press.
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