  In this work, we present a novel multi-modal multi-agent trajectory
prediction architecture, focusing on map and interaction modeling using graph
representation. For the purposes of map modeling, we capture rich topological
structure into vector-based star graphs, which enable an agent to directly
attend to relevant regions along polylines that are used to represent the map.
We denote this architecture StarNet, and integrate it in a single-agent
prediction setting. As the main result, we extend this architecture to joint
scene-level prediction, which produces multiple agents' predictions
simultaneously. The key idea in joint-StarNet is integrating the awareness of
one agent in its own reference frame with how it is perceived from the points
of view of other agents. We achieve this via masked self-attention. Both
proposed architectures are built on top of the action-space prediction
framework introduced in our previous work, which ensures kinematically feasible
trajectory predictions. We evaluate the methods on the interaction-rich inD and
INTERACTION datasets, with both StarNet and joint-StarNet achieving
improvements over state of the art.
