Act Natural! Extending Naturalistic Projection to Multimodal Behavior Scenarios

--Autonomous agents operating in public spaces must consider how their behaviors might affect the humans around them, even when not directly interacting with them. T o this end, it is often beneficial to be predictable and appear naturalistic. Existing methods for this purpose use human actor intent modeling or imitation learning techniques, but these approaches rarely capture all possible motivations for human behavior and/or require significant amounts of data. Our work extends a technique for modeling unimodal naturalistic behaviors with an explicit convex set representation, to account for multimodal behavior by using multiple convex sets. This more flexible representation provides a higher degree of fidelity in data-driven modeling of naturalistic behavior that arises in real-world scenarios in which human behavior is, in some sense, discrete, e.g. Equipped with this new set representation, we develop an optimization-based filter to project arbitrary trajectories into the set so that they appear naturalistic to humans in the scene, while also satisfying vehicle dynamics, actuator limits, etc. We demonstrate our methods on real-world human driving data from the inD (intersection) and rounD (roundabout) datasets. Safe and comfortable interaction between humans and autonomous agents requires a measure of predictable and naturalistic behavior from autonomous systems. Autonomous agents in the real world can easily find themselves in unsafe situations when they violate these informal norms of humanlike behavior. As one example, autonomous vehicles are well-documented to behave more cautiously than human drivers expect, which can lead to human drivers reacting unsafely to unexpected or abnormal driving and causing collisions [1]. Thus, autonomous agents must be able to plan and execute naturalistic, human-like behavior. However, naturalistic behavior tends to be challenging to model mathematically because human preferences and decision-making are opaque. Nevertheless, there exists a need for techniques which are able to model the wide variety of naturalistic behavior based on observations of human actions. This work was supported by the National Science Foundation under Grants 2211548 and 2336840. Each nonconvex set is represented by the union of a set of convex hulls which are formed by clustering the trajectory states at each time. Then, we project arbitrary trajectories into this set to make the behaviors more naturalistic.