  The development of algorithms that learn multi-agent behavioral models using
human demonstrations has led to increasingly realistic simulations in the field
of autonomous driving. In general, such models learn to jointly predict
trajectories for all controlled agents by exploiting road context information
such as drivable lanes obtained from manually annotated high-definition (HD)
maps. Recent studies show that these models can greatly benefit from increasing
the amount of human data available for training. However, the manual annotation
of HD maps which is necessary for every new location puts a bottleneck on
efficiently scaling up human traffic datasets. We propose an aerial image-based
map (AIM) representation that requires minimal annotation and provides rich
road context information for traffic agents like pedestrians and vehicles. We
evaluate multi-agent trajectory prediction using the AIM by incorporating it
into a differentiable driving simulator as an image-texture-based
differentiable rendering module. Our results demonstrate competitive
multi-agent trajectory prediction performance especially for pedestrians in the
scene when using our AIM representation as compared to models trained with
rasterized HD maps.
