Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning Framework

Predicting distant future trajectories of agents in a dynamic scene is not an easy problem because the future trajectory of an agent is affected by not only his/her past trajectory but also the scene contexts. To tackle this problem, we propose a model based on recurrent neural networks (RNNs) and a...

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Veröffentlicht in:	arXiv.org 2019-12
Hauptverfasser:	Choi, Dooseop, Kyoungwook Min, Choi, Jeongdan
Format:	Artikel
Sprache:	eng
Schlagworte:	Encoders-Decoders Neural networks Predictions Recurrent neural networks Regularization Trajectory analysis
Online-Zugang:	Volltext
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