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The task of predicting stochastic behaviors of road agents in diverseenvironments is a challenging problem for autonomous driving. To bestunderstand scene contexts and produce diverse possible future states of theroad agents adaptively in different environments, a prediction model should beprobabilistic, multi-modal, context-driven, and general. We presentConditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); aconditional VAE that extensively leverages hypernetwork and performs generativetasks for high-dimensional problems like the prediction task. We first evaluateCVAE-H on simple generative experiments to show that CVAE-H is probabilistic,multi-modal, context-driven, and general. Then, we demonstrate that theproposed model effectively solves a self-driving prediction problem byproducing accurate predictions of road agents in various environments.

CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving