Journal of Epidemiology and Public Health

Road Width Estimation Using Multi Camera Deep Learning Framework in Unmarked Indian Roads: Quantitative Modeling for Economic Prediction

Abstract

Manas Kumar Rath Manas, Prasanta Kumar Swain Prasanta, and Suman Majumder Suman

The accurate estimation of road widths on unmarked and un- structured roads is critical for Advanced Driver Assistance Systems (ADAS), autonomous navigation, and intelligent transportation plan- ning. In Indian driving conditions, irregular boundaries and missing lane markings make conventional lane detection ineffective. We pro- pose a multi-camera framework integrating a dashboard monocular camera with dual Outside Rear View Mirror (ORVM) cameras for dynamic road-width estimation. The scheme employs deep learning segmentation to extract road boundaries and fuses monocular depth and stereo disparity using a Kalman-based dynamic model. From a quantitative perspective, the recursive Kalman fusion functions anal- ogously data-driven optimization for safe, efficient transportation systems. to dynamic optimization and equilibrium modeling in ‘Quan- titative Economics, allowing predictive analysis of traffic density, flow efficiency, and infrastructure utilization. Trained on a diverse ur- ban–rural Indian road dataset, the model achieves high estimation ac- curacy and predictive stability, demonstrating improved performance over single-camera baselines and enabling 

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