  Despite growing attention in autonomy, there are still many open problems,
including how autonomous vehicles will interact and communicate with other
agents, such as human drivers and pedestrians. Unlike most approaches that
focus on pedestrian detection and planning for collision avoidance, this paper
considers modeling the interaction between human drivers and pedestrians and
how it might influence map estimation, as a proxy for detection. We take a
mapping inspired approach and incorporate people as sensors into mapping
frameworks. By taking advantage of other agents' actions, we demonstrate how we
can impute portions of the map that would otherwise be occluded. We evaluate
our framework in human driving experiments and on real-world data, using
occupancy grids and landmark-based mapping approaches. Our approach
significantly improves overall environment awareness and out-performs standard
mapping techniques.
