Trajectory Prediction with Graph-based Dual-scale Context Fusion
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S. Shen | Jing Chen | Lu Zhang | Peiliang Li
[1] Fabien Moutarde,et al. GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[2] Lu Zhang,et al. EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments , 2021, IEEE Transactions on Robotics.
[3] Omar Y. Al-Jarrah,et al. Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review , 2019, ArXiv.
[4] Hang Zhao,et al. DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] Fabien Moutarde,et al. HOME: Heatmap Output for future Motion Estimation , 2021, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).
[6] Jiquan Ngiam,et al. Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[7] Bolei Zhou,et al. Multimodal Motion Prediction with Stacked Transformers , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Qifeng Chen,et al. Learning to Predict Vehicle Trajectories with Model-based Planning , 2021, CoRL.
[9] Renjie Liao,et al. LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting , 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[10] Micol Marchetti-Bowick,et al. Map-Adaptive Goal-Based Trajectory Prediction , 2020, CoRL.
[11] Yi Shen,et al. TNT: Target-driveN Trajectory Prediction , 2020, CoRL.
[12] R. Urtasun,et al. Learning Lane Graph Representations for Motion Forecasting , 2020, ECCV.
[13] Alan Yuille,et al. Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[14] Dragomir Anguelov,et al. VectorNet: Encoding HD Maps and Agent Dynamics From Vectorized Representation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] J. Malik,et al. It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction , 2020, ECCV.
[16] Freddy A. Boulton,et al. CoverNet: Multimodal Behavior Prediction Using Trajectory Sets , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Dariu M. Gavrila,et al. Human motion trajectory prediction: a survey , 2019, Int. J. Robotics Res..
[18] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Benjamin Sapp,et al. MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction , 2019, CoRL.
[20] Simon Lucey,et al. Argoverse: 3D Tracking and Forecasting With Rich Maps , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Ying Nian Wu,et al. Multi-Agent Tensor Fusion for Contextual Trajectory Prediction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Henggang Cui,et al. Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[23] Hei Law,et al. CornerNet: Detecting Objects as Paired Keypoints , 2018, International Journal of Computer Vision.
[24] Mohan M. Trivedi,et al. Convolutional Social Pooling for Vehicle Trajectory Prediction , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[25] Silvio Savarese,et al. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[27] Jean Oh,et al. Social Attention: Modeling Attention in Human Crowds , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[28] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[29] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[30] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[31] Philip H. S. Torr,et al. DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[34] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[35] Geoffrey E. Hinton,et al. Layer Normalization , 2016, ArXiv.
[36] Silvio Savarese,et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[39] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[40] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[41] Dizan Vasquez,et al. A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.
[42] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[43] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[44] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.