Typography-Based Monocular Distance Estimation Framework for Vehicle Safety Systems

Accurate inter-vehicle distance estimation is a cornerstone of advanced driver assistance systems and autonomous driving. While LiDAR and radar provide high precision, their cost prohibits widespread adoption in mass-market vehicles. Monocular vision offers a low-cost alternative but suffers from scale ambiguity and sensitivity to environmental disturbances. This paper introduces a typography-based monocular distance estimation framework that exploits the standardized typography of license plates as passive fiducial markers for metric distance estimation. The core geometric module uses robust plate detection and character segmentation to measure character height and computes distance via the pinhole camera model. The system incorporates interactive calibration, adaptive detection, camera pose compensation, hybrid deep-learning fusion, temporal Kalman filtering, and multi-feature fusion. Experimental validation achieved a coefficient of variation of 2.3% in character height and a mean absolute error of 7.7%, with character-based ranging reducing estimate standard deviation by 35% compared with plate-width methods.