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.

本文提出一种使用弱监督学习的方法，通过对车辆记录路段进行标注，并训练语义分割网络，以实现在复杂城市环境下使用单目摄像头自动驾驶，成功在 KITTI 和 Oxford RobotCar 数据集下实现了路径提议和障碍物分割，并在不同的天气、光线和交通条件下展示出可靠的应用性，可泛化到多种路径提议的情况，并提出将上述系统整合到一个自主城市驾驶框架中的计划。