Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography

Accurate inter-vehicle distance estimation is a cornerstone of advanced driver assistance systems and autonomous driving. While LiDAR and radar provide high precision, their high cost prohibits widespread adoption in mass-market vehicles. Monocular camera-based estimation offers a low-cost alternative but suffers from fundamental scale ambiguity. This paper presents a framework that exploits the standardized typography of United States license plates as passive fiducial markers for metric ranging, resolving scale ambiguity through explicit geometric priors without training data or active illumination. A four-method parallel plate detector, a three-stage state identification engine, and hybrid depth fusion with inverse-variance weighting and a constant-velocity Kalman filter deliver smoothed distance, relative velocity, and time-to-collision. Controlled outdoor experiments over 3,263 frames confirm a mean absolute error of 2.3% at 10 m, continuous distance output during brief occlusions, and 94.7% state identification accuracy.