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

Source: [Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy](<https://www.sota2.com/research/paper/68fa63d41e209131cea0111f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:50:10.530Z

Authors: Dan Barnes, Will Maddern, Ingmar Posner

Published: 2016-10-05T00:44:49.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1610.01238>)

[PDF](<https://arxiv.org/pdf/1610.01238.pdf>)