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Katie Luo
Researcher at Uber
Publications - 3
Citations - 115
Katie Luo is an academic researcher from Uber . The author has contributed to research in topics: Graph (abstract data type) & Latent variable model. The author has an hindex of 2, co-authored 3 publications receiving 55 citations.
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Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
TL;DR: In this article, the authors use graph neural networks to learn a distributed latent representation of the scene and obtain trajectory samples that are consistent across traffic participants, achieving state-of-the-art results in motion forecasting and interaction understanding.
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Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
TL;DR: This paper proposes to characterize the joint distribution over future trajectories via an implicit latent variable model and model the scene as an interaction graph and employs powerful graph neural networks to learn a distributed latent representation of the scene.
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Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting.
TL;DR: In this paper, a scene actor graph neural network (SA-GNN) is proposed to capture the interactions among pedestrians within the same scene, including those that have not been detected, via message passing.