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• W2894978157 abstract "We propose a method to forecast a vehicle’s ego-motion as a distribution over spatiotemporal paths, conditioned on features (e.g., from LIDAR and images) embedded in an overhead map. The method learns a policy inducing a distribution over simulated trajectories that is both “diverse” (produces most of the likely paths) and “precise” (mostly produces likely paths). This balance is achieved through minimization of a symmetrized cross-entropy between the distribution and demonstration data. By viewing the simulated-outcome distribution as the pushforward of a simple distribution under a simulation operator, we obtain expressions for the cross-entropy metrics that can be efficiently evaluated and differentiated, enabling stochastic-gradient optimization. We propose concrete policy architectures for this model, discuss our evaluation metrics relative to previously-used degenerate metrics, and demonstrate the superiority of our method relative to state-of-the-art methods in both the Kitti dataset and a similar but novel and larger real-world dataset explicitly designed for the vehicle forecasting domain." @default.
• W2894978157 created "2018-10-12" @default.
• W2894978157 creator A5037322163 @default.
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• W2894978157 date "2018-01-01" @default.
• W2894978157 modified "2023-10-05" @default.
• W2894978157 title "r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting" @default.
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• W2894978157 doi "https://doi.org/10.1007/978-3-030-01261-8_47" @default.
• W2894978157 hasPublicationYear "2018" @default.
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