@inproceedings{Zhang2013ICCV,
  title = {Understanding High-Level Semantics by Modeling Traffic Patterns},
  booktitle = {International Conference on Computer Vision},
  abstract = {In this paper, we are interested in understanding the semantics of
   outdoor scenes in the context of autonomous driving. Towards this
   goal, we propose a generative model of 3D urban scenes which is able
    to reason not only about the geometry and objects present in the
    scene, but also about the high-level semantics in the form of traffic
   patterns. We found that a small number of patterns is sufficient
    to model the vast majority of traffic scenes and show how these patterns
    can be learned. As evidenced by our experiments, this high-level
    reasoning significantly improves the overall scene estimation as
    well as the vehicle-to-lane association when compared to state-of-the-art
   approaches. All data and code will be made available upon publication.},
  pages = {3056-3063},
  address = {Sydney, Australia},
  month = dec,
  year = {2013},
  slug = {zhang2013iccv},
  author = {Zhang, Hongyi and Geiger, Andreas and Urtasun, Raquel},
  month_numeric = {12}
}