Recently, vision-based Advanced Driver Assist Systems have gained broad
interest. In this work, we investigate free-space detection, for which we
propose to employ a Fully Convolutional Network (FCN). We show that this FCN
can be trained in a self-supervised manner and achieve similar results compared
to training on manually annotated data, thereby reducing the need for large
manually annotated training sets. To this end, our self-supervised training
relies on a stereo-vision disparity system, to automatically generate (weak)
training labels for the color-based FCN. Additionally, our self-supervised
training facilitates online training of the FCN instead of offline.
Consequently, given that the applied FCN is relatively small, the free-space
analysis becomes highly adaptive to any traffic scene that the vehicle
encounters. We have validated our algorithm using publicly available data and
on a new challenging benchmark dataset that is released with this paper.
Experiments show that the online training boosts performance with 5% when
compared to offline training, both for Fmax and AP.

本文研究了基于视觉的高级驾驶辅助系统中的自由空间检测，提出采用完全卷积网络进行自我监督训练，以降低对大型手动注释训练集的需求。通过使用立体视觉视差系统自动生成 FCN（Fully Convolutional Network）的（弱）训练标签，使得应用 FCN 能够适应任何车辆遇到的交通场景。在线训练 FCN 的结果表明，相比于离线训练，Fmax 和 AP 的性能提升了 5%。