  While most approaches to semantic reasoning have focused on improving
performance, in this paper we argue that computational times are very important
in order to enable real time applications such as autonomous driving. Towards
this goal, we present an approach to joint classification, detection and
semantic segmentation via a unified architecture where the encoder is shared
amongst the three tasks. Our approach is very simple, can be trained end-to-end
and performs extremely well in the challenging KITTI dataset, outperforming the
state-of-the-art in the road segmentation task. Our approach is also very
efficient, taking less than 100 ms to perform all tasks.
