  One of the key challenges for autonomous vehicles is the ability to
accurately predict the motion of other objects in the surrounding environment,
such as pedestrians or other vehicles. In this contribution, a novel motion
forecasting approach for autonomous vehicles is developed, inspired by the work
of Gilles et al. [1]. We predict multiple heatmaps with a neuralnetwork-based
model for every traffic participant in the vicinity of the autonomous vehicle;
with one heatmap per timestep. The heatmaps are used as input to a novel
sampling algorithm that extracts coordinates corresponding to the most likely
future positions. We experiment with different encoders and decoders, as well
as a comparison of two loss functions. Additionally, a new grid-scaling
technique is introduced, showing further improved performance. Overall, our
approach improves stateof-the-art miss rate performance for the
function-relevant prediction interval of 3 seconds while being competitive in
longer prediction intervals (up to eight seconds). The evaluation is done on
the public 2022 Waymo motion challenge.
