Relation-based Motion Prediction using Traffic Scene Graphs. (arXiv:2212.02503v1 [cs.AI])

Representing relevant information of a traffic scene and understanding its
environment is crucial for the success of autonomous driving. Modeling the
surrounding of an autonomous car using semantic relations, i.e., how different
traffic participants relate in the context of traffic rule based behaviors, is
hardly been considered in previous work. This stems from the fact that these
relations are hard to extract from real-world traffic scenes. In this work, we
model traffic scenes in a form of spatial semantic scene graphs for various
different predictions about the traffic participants, e.g., acceleration and
deceleration. Our learning and inference approach uses Graph Neural Networks
(GNNs) and shows that incorporating explicit information about the spatial
semantic relations between traffic participants improves the predicdtion
results. Specifically, the acceleration prediction of traffic participants is
improved by up to 12% compared to the baselines, which do not exploit this
explicit information. Furthermore, by including additional information about
previous scenes, we achieve 73% improvements.