Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy

Abstract – We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large- scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.

  • [PDF] D. Barnes, W. Maddern, and I. Posner, “Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017.
    [Bibtex]

    @inproceedings{BarnesICRA2017,
    Address = {Singapore},
    Author = {Barnes, Dan and Maddern, Will and Posner, Ingmar},
    Booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
    Month = {June},
    Pdf = {https://arxiv.org/pdf/1610.01238v2},
    URL = {https://arxiv.org/abs/1610.01238},
    Title = "{Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy}",
    Year = {2017}}