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Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning Framework
[article]
2019
arXiv
pre-print
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 novel method for training the model. The proposed model is based on an encoder-decoder architecture where the encoder encodes inputs (past trajectories and scene context information) while the
arXiv:1907.04525v2
fatcat:z4k4jljz4vc7ber362oqifinlu