  Precisely predicting the future trajectories of surrounding traffic
participants is a crucial but challenging problem in autonomous driving, due to
complex interactions between traffic agents, map context and traffic rules.
Vector-based approaches have recently shown to achieve among the best
performances on trajectory prediction benchmarks. These methods model simple
interactions between traffic agents but don't distinguish between relation-type
and attributes like their distance along the road. Furthermore, they represent
lanes only by sequences of vectors representing center lines and ignore context
information like lane dividers and other road elements. We present a novel
approach for vector-based trajectory prediction that addresses these
shortcomings by leveraging three crucial sources of information: First, we
model interactions between traffic agents by a semantic scene graph, that
accounts for the nature and important features of their relation. Second, we
extract agent-centric image-based map features to model the local map context.
Finally, we generate anchor paths to enforce the policy in multi-modal
prediction to permitted trajectories only. Each of these three enhancements
shows advantages over the baseline model HoliGraph.
