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We present a weakly-supervised approach to segmenting proposed drivable pathsin images with the goal of autonomous driving in complex urban environments.Using recorded routes from a data collection vehicle, our proposed methodgenerates vast quantities of labelled images containing proposed paths andobstacles without requiring manual annotation, which we then use to train adeep semantic segmentation network. With the trained network we can segmentproposed paths and obstacles at run-time using a vehicle equipped with only amonocular camera without relying on explicit modelling of road or lanemarkings. We evaluate our method on the large-scale KITTI and Oxford RobotCardatasets and demonstrate reliable path proposal and obstacle segmentation in awide variety of environments under a range of lighting, weather and trafficconditions. We illustrate how the method can generalise to multiple pathproposals at intersections and outline plans to incorporate the system into aframework for autonomous urban driving.

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