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R2P2: A Reparameterized Pushforward Policy for Diverse, Precise Generative Path Forecasting
ECCV 2018 | We propose a method to forecast a vehicle’s ego-motion as a distribution over spatiotemporal paths, conditioned on features embedded in an overhead map. The method learns a policy and induces a distribution over simulated trajectories that is both “diverse” (produces most paths likely under the data) and “precise” (mostly produces paths likely under the data). We achieve this balance through minimization of a symmetrized cross-entropy between the distribution and demonstration data.
Collaborators: Nicholas Rhinehart, Kris M. Kitani, Paul Vernaza
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