  Both object detection in and semantic segmentation of camera images are
important tasks for automated vehicles. Object detection is necessary so that
the planning and behavior modules can reason about other road users. Semantic
segmentation provides for example free space information and information about
static and dynamic parts of the environment. There has been a lot of research
to solve both tasks using Convolutional Neural Networks. These approaches give
good results but are computationally demanding. In practice, a compromise has
to be found between detection performance, detection quality and the number of
tasks. Otherwise it is not possible to meet the real-time requirements of
automated vehicles. In this work, we propose a neural network architecture to
solve both tasks simultaneously. This architecture was designed to run with
around 10 Hz on 1 MP images on current hardware. Our approach achieves a mean
IoU of 61.2% for the semantic segmentation task on the challenging Cityscapes
benchmark. It also achieves an average precision of 69.3% for cars and 67.7% on
the moderate difficulty level of the KITTI benchmark.
