We present a scalable self-supervised approach for segmenting feasible vehicle trajectories from monocular images for autonomous driving in complex urban environments. Our method leverages large-scale dashcam videos, treating recorded ego-vehicle motion as implicit supervision to recover camera trajectories via monocular structure-from-motion. These trajectories are projected onto the ground plane to generate spatial masks of traversed regions without manual annotation. We train a deep segmentation network that predicts motion-conditioned path proposals from a single RGB image at runtime, without explicit modeling of road or lane markings. The model implicitly captures scene layout, lane topology, and intersection structure, demonstrating generalization across varying camera configurations. We evaluate on NuScenes for reliable trajectory prediction and show transfer capability to an electric scooter platform.