  In autonomous driving, accurately interpreting the movements of other road
users and leveraging this knowledge to forecast future trajectories is crucial.
This is typically achieved through the integration of map data and tracked
trajectories of various agents. Numerous methodologies combine this information
into a singular embedding for each agent, which is then utilized to predict
future behavior. However, these approaches have a notable drawback in that they
may lose exact location information during the encoding process. The encoding
still includes general map information. However, the generation of valid and
consistent trajectories is not guaranteed. This can cause the predicted
trajectories to stray from the actual lanes. This paper introduces a new
refinement module designed to project the predicted trajectories back onto the
actual map, rectifying these discrepancies and leading towards more consistent
predictions. This versatile module can be readily incorporated into a wide
range of architectures. Additionally, we propose a novel scene encoder that
handles all relations between agents and their environment in a single unified
heterogeneous graph attention network. By analyzing the attention values on the
different edges in this graph, we can gain unique insights into the neural
network's inner workings leading towards a more explainable prediction.
