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The development of algorithms that learn multi-agent behavioral models usinghuman demonstrations has led to increasingly realistic simulations in the fieldof autonomous driving. In general, such models learn to jointly predicttrajectories for all controlled agents by exploiting road context informationsuch as drivable lanes obtained from manually annotated high-definition (HD)maps. Recent studies show that these models can greatly benefit from increasingthe amount of human data available for training. However, the manual annotationof HD maps which is necessary for every new location puts a bottleneck onefficiently scaling up human traffic datasets. We propose an aerial image-basedmap (AIM) representation that requires minimal annotation and provides richroad context information for traffic agents like pedestrians and vehicles. Weevaluate multi-agent trajectory prediction using the AIM by incorporating itinto a differentiable driving simulator as an image-texture-baseddifferentiable rendering module. Our results demonstrate competitivemulti-agent trajectory prediction performance especially for pedestrians in thescene when using our AIM representation as compared to models trained withrasterized HD maps.

Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images