  Besides interacting correctly with other vehicles, automated vehicles should
also be able to react in a safe manner to vulnerable road users like
pedestrians or cyclists. For a safe interaction between pedestrians and
automated vehicles, the vehicle must be able to interpret the pedestrian's
behavior. Common environment models do not contain information like body poses
used to understand the pedestrian's intent. In this work, we propose an
environment model that includes the position of the pedestrians as well as
their pose information. We only use images from a monocular camera and the
vehicle's localization data as input to our pedestrian environment model. We
extract the skeletal information with a neural network human pose estimator
from the image. Furthermore, we track the skeletons with a simple tracking
algorithm based on the Hungarian algorithm and an ego-motion compensation. To
obtain the 3D information of the position, we aggregate the data from
consecutive frames in conjunction with the vehicle position. We demonstrate our
pedestrian environment model on data generated with the CARLA simulator and the
nuScenes dataset. Overall, we reach a relative position error of around 16% on
both datasets.
