Driver Identification and Activity Tracking with Geo-Fencing & Number KM Lock Using Python and Raspberry Pi

Abstract: This paper presents an enhanced intelligent vehicle monitoring and security system integrating multi-factor driver authentication, real-time GPS tracking, geo-fencing, and distance-based vehicle locking using Raspberry Pi and Python. Unlike traditional systems, the proposed model incorporates biometric fingerprint matching, face-recognition-based driver identification, and single-bit authentication to activate vehicle ignition. The system continuously collects GPS and sensor data to monitor vehicle movement, enforce geo-fence boundaries, and automatically restrict operation when predefined KM limits are exceeded. AI-enabled modules further support driver activity prediction and periodic in-drive re-authentication to prevent unauthorized access, misuse, and safety risks. Experimental implementation confirms reliable performance across real-time tracking, identity verification, geo-fence breach response, and automated lock mechanisms, demonstrating strong potential for applications in fleet monitoring, rental automation, and intelligent transport management.

Keywords: Fleet Management, Theft Prevention, GPS Tracking, Real-time Monitoring, Microcontroller Automation.