  Autonomous agents operating around human actors must consider how their
behaviors might affect those humans, even when not directly interacting with
them. To this end, it is often beneficial to be predictable and appear
naturalistic. Existing methods to address this problem use human actor intent
modeling or imitation learning techniques, but these approaches rarely capture
all possible motivations for human behavior or require significant amounts of
data. In contrast, we propose a technique for modeling naturalistic behavior as
a set of convex hulls computed over a relatively small dataset of human
behavior. Given this set, we design an optimization-based filter which projects
arbitrary trajectories into it to make them more naturalistic for autonomous
agents to execute while also satisfying dynamics constraints. We demonstrate
our methods on real-world human driving data from the inD intersection dataset
(Bock et al., 2020).
