Recent progress in vectorized HD-map extraction has been driven by end-to-end transformer architectures such as MapTR and MapTR-v2, which typically assume access to synchronized multi-view cameras. In this work, we extend MapTR to the monocular setting, enabling the detection of map elements from a single front-facing camera. To achieve this, we introduce three modifications to the standard pipeline: (i) a camera-aware BEV lifting stage conditioned on learnable view tokens; (ii) the integration of monocular depth priors obtained from MiDaS, discretized into an uncertainty-aware cost volume that guides BEV features; and (iii) a training strategy tailored to monocular coverage, incorporating temporal and geometric augmentations together with a short-cycle schedule that ensures stable convergence without the need for warmup.