  Autonomous vehicles must reason about spatial occlusions in urban
environments to ensure safety without being overly cautious. Prior work
explored occlusion inference from observed social behaviors of road agents,
hence treating people as sensors. Inferring occupancy from agent behaviors is
an inherently multimodal problem; a driver may behave similarly for different
occupancy patterns ahead of them (e.g., a driver may move at constant speed in
traffic or on an open road). Past work, however, does not account for this
multimodality, thus neglecting to model this source of aleatoric uncertainty in
the relationship between driver behaviors and their environment. We propose an
occlusion inference method that characterizes observed behaviors of human
agents as sensor measurements, and fuses them with those from a standard sensor
suite. To capture the aleatoric uncertainty, we train a conditional variational
autoencoder with a discrete latent space to learn a multimodal mapping from
observed driver trajectories to an occupancy grid representation of the view
ahead of the driver. Our method handles multi-agent scenarios, combining
measurements from multiple observed drivers using evidential theory to solve
the sensor fusion problem. Our approach is validated on a cluttered, real-world
intersection, outperforming baselines and demonstrating real-time capable
performance. Our code is available at
https://github.com/sisl/MultiAgentVariationalOcclusionInference .
