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GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction (2402.19002v1)

Published 29 Feb 2024 in cs.CV and cs.AI

Abstract: Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian's intentions and decision-making, which is a multi-modal problem. Most recent studies use past trajectories to predict a variety of potential future trajectory distributions, which do not account for the scene context and pedestrian targets. Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories. By leveraging the information from scene context and observed trajectory, the uncertainty can be limited to a few target areas, which represent the "goals" of the pedestrians. In this paper, we propose GoalNet, a new trajectory prediction neural network based on the goal areas of a pedestrian. Our network can predict both pedestrian's trajectories and bounding boxes. The overall model is efficient and modular, and its outputs can be changed according to the usage scenario. Experimental results show that GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset.

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References (39)
  1. Social lstm: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 961–971, 2016.
  2. Stochastic sampling simulation for pedestrian trajectory prediction. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4236–4243, 2019.
  3. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.
  4. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11621–11631, 2020.
  5. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In Leslie Pack Kaelbling, Danica Kragic, and Komei Sugiura, editors, Proceedings of the Conference on Robot Learning (CoRL), volume 100 of Proceedings of Machine Learning Research, pages 86–99. PMLR, 30 Oct–01 Nov 2020.
  6. Argoverse: 3d tracking and forecasting with rich maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8748–8757, 2019.
  7. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. Proceedings of the IEEE transactions on pattern analysis and machine intelligence, 40(4):834–848, 2017.
  8. On-board pedestrian trajectory prediction using behavioral features. In Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA), pages 437–443, 2022.
  9. Deep spatial autoencoders for visuomotor learning. In IEEE International Conference on Robotics and Automation (ICRA), page 512–519. IEEE Press, 2016.
  10. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1440–1448, 2015.
  11. Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5496–5506, 2023.
  12. End-to-end trajectory distribution prediction based on occupancy grid maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2242–2251, 2022.
  13. Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2255–2264, 2018.
  14. Action-based contrastive learning for trajectory prediction. In Proceedings of the European Conference on Computer Vision (ECCV), page 143–159. Springer-Verlag, 2022.
  15. Deep residual learning for image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016.
  16. Identity mappings in deep residual networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 630–645. Springer, 2016.
  17. Sergey Ioffe. Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. Advances in neural information processing systems, 30, 2017.
  18. The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 2375–2384, 2019.
  19. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), San Diega, CA, USA, 2015.
  20. Crowds by example. In Proceedings of the Computer Graphics Forum (CGF), pages 655–664. Wiley Online Library, 2007.
  21. Simaug: Learning robust representations from simulation for trajectory prediction. In In Proceedings of the European Confer- ence on Computer Vision (ECCV), page 275–292, Berlin, Heidelberg, 2020. Springer-Verlag.
  22. The garden of forking paths: Towards multi-future trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10508–10518, 2020.
  23. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11976–11986, 2022.
  24. Dropout reduces underfitting. In Proceedings of the 40th International Conference on Machine Learning (ICML). JMLR.org, 2023.
  25. From goals, waypoints & paths to long term human trajectory forecasting. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15233–15242, 2021.
  26. Attention u-net: Learning where to look for the pancreas. In Medical Imaging with Deep Learning, 2018.
  27. Improving data association by joint modeling of pedestrian trajectories and groupings. In Proceedings of the European Conference on Computer Vision (ECCV), pages 452–465. Springer, 2010.
  28. Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
  29. Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 206–213, 2017.
  30. Learning social etiquette: Human trajectory understanding in crowded scenes. In Proceedings of the European Conference on Computer Vision (ECCV), pages 549–565. ”Springer, 2016.
  31. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 234–241. Springer, 2015.
  32. Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In Proceedings of the European Conference on Computer Vision (ECCV), pages 683–700. Springer, 2020.
  33. Evostgat: Evolving spatiotemporal graph attention networks for pedestrian trajectory prediction. Neurocomputing, 491:333–342, 2022.
  34. Stepwise goal-driven networks for trajectory prediction. IEEE Robotics and Automation Letters, 7(2):2716–2723, 2022.
  35. View vertically: A hierarchical network for trajectory prediction via fourier spectrums. In Proceedings of the European Conference on Computer Vision (ECCV), pages 682–700. Springer, 2022.
  36. Group normalization. In Proceedings of the European Conference on Computer Vision (ECCV), pages 3–19, 2018.
  37. Poppl: Pedestrian trajectory prediction by lstm with automatic route class clustering. IEEE transactions on neural networks and learning systems, 32(1):77–90, 2020.
  38. Bitrap: Bi-directional pedestrian trajectory prediction with multi-modal goal estimation. IEEE Robotics and Automation Letters, 6(2):1463–1470, 2021.
  39. Human trajectory prediction via neural social physics. In Proceedings of the European Conference on Computer Vision (ECCV), pages 376–394. Springer, 2022.

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