  This work introduces a new approach for joint detection of centerlines based
on image data by localizing the features jointly in 2D and 3D. In contrast to
existing work that focuses on detection of visual cues, we explore feature
extraction methods that are directly amenable to the urban driving task. To
develop and evaluate our approach, a large urban driving dataset dubbed AV
Breadcrumbs is automatically labeled by leveraging vector map representations
and projective geometry to annotate over 900,000 images. Our results
demonstrate potential for dynamic scene modeling across various urban driving
scenarios. Our model achieves an F1 score of 0.684 and an average normalized
depth error of 2.083. The code and data annotations are publicly available.
