AI Powered Vision Transformer-Based Solution for Real-Time Illegal Driver Substitution Detection in Ride-Hailing Services

Ride-hailing services provides convenient, affordable, and on-demand travel options. However, the rapid expansion of these platforms has introduced security challenges, including illegal driver substitution. This issue arises when unauthorized individuals operate as drivers, jeopardizing passenger safety. Detecting such substitutions remains a significant challenge due to the lack of effective real-time verification mechanisms. Existing systems primarily depend on manual identity verification methods, such as document checks or static photograph comparisons. These methods are susceptible to manipulation and fail to ensure continuous monitoring during rides, leaving significant security gaps. To overcome these limitations, this project introduces a solution that leverages face biometrics and the Vision Transformer model. The system employs real-time facial recognition to verify drivers during rides, utilizing ViT 's advanced capability to capture intricate facial features with high precision under diverse conditions, including varying lighting, angles, and expressions. A centralized biometric database ensures secure storage. Alerts are triggered in case of unauthorized driver detection, ensuring a prompt response. This approach enhances the security framework of ride-hailing services by delivering continuous, automated, and reliable driver authentication. It mitigates the risks associated with illegal driver substitution, fostering a safer and more trustworthy environment for passengers and service providers.