Act Natural! Extending Naturalistic Projection to Multimodal Behavior Scenarios

TL;DR

This paper introduces a multimodal naturalistic behavior modeling technique using multiple convex sets, enabling autonomous agents to generate more human-like, predictable behaviors in complex real-world scenarios.

Contribution

It extends existing unimodal behavior modeling to multimodal scenarios with convex sets, improving the fidelity of naturalistic behavior projection in autonomous systems.

Findings

Effective modeling of multimodal human behaviors in driving data

Projection method produces naturalistic, human-like trajectories

Demonstrated on real-world intersection and roundabout datasets

Abstract

Autonomous agents operating in public spaces must consider how their behaviors might affect the humans around them, even when not directly interacting with them. To 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. whether or not to yield at a roundabout. 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.