HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling

HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling

Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the i...

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Bibliographic Details
Main Authors: Huang, Xin, Rosman, Guy, Gilitschenski, Igor, Jasour, Ashkan, McGill, Stephen G., Leonard, John J., Williams, Brian C.
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: IEEE 2024
Online Access:https://hdl.handle.net/1721.1/153756

Internet

https://hdl.handle.net/1721.1/153756

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