  In this article, an approach for probabilistic trajectory forecasting of
vulnerable road users (VRUs) is presented, which considers past movements and
the surrounding scene. Past movements are represented by 3D poses reflecting
the posture and movements of individual body parts. The surrounding scene is
modeled in the form of semantic maps showing, e.g., the course of streets,
sidewalks, and the occurrence of obstacles. The forecasts are generated in
grids discretizing the space and in the form of arbitrary discrete probability
distributions. The distributions are evaluated in terms of their reliability,
sharpness, and positional accuracy. We compare our method with an approach that
provides forecasts in the form of Gaussian distributions and discuss the
respective advantages and disadvantages. Thereby, we investigate the impact of
using poses and semantic maps. With a technique called spatial label smoothing,
our approach achieves reliable forecasts. Overall, the poses have a positive
impact on the forecasts. The semantic maps offer the opportunity to adapt the
probability distributions to the individual situation, although at the
considered forecasted time horizon of 2.52 s they play a minor role compared to
the past movements of the VRU. Our method is evaluated on a dataset recorded in
inner-city traffic using a research vehicle. The dataset is made publicly
available.
