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Adapting to Length Shift: FlexiLength Network for Trajectory Prediction (2404.00742v1)

Published 31 Mar 2024 in cs.CV

Abstract: Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. However, a notable issue arises when these models are evaluated with varying observation lengths, leading to a significant performance drop, a phenomenon we term the Observation Length Shift. To address this issue, we introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction techniques against varying observation periods. Specifically, FLN integrates trajectory data with diverse observation lengths, incorporates FlexiLength Calibration (FLC) to acquire temporal invariant representations, and employs FlexiLength Adaptation (FLA) to further refine these representations for more accurate future trajectory predictions. Comprehensive experiments on multiple datasets, ie, ETH/UCY, nuScenes, and Argoverse 1, demonstrate the effectiveness and flexibility of our proposed FLN framework.

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References (77)
  1. Social ways: Learning multi-modal distributions of pedestrian trajectories with GANs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 2964–2972, 2019.
  2. Adapt: Efficient multi-agent trajectory prediction with adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8295–8305, 2023.
  3. Learning pedestrian group representations for multi-modal trajectory prediction. In Proceedings of the European Conference on Computer Vision, pages 270–289, 2022.
  4. Eigentrajectory: Low-rank descriptors for multi-modal trajectory forecasting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10017–10029, 2023.
  5. Vehicle trajectory prediction works, but not everywhere. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17123–17133, 2022.
  6. nuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11621–11631, 2020.
  7. Argoverse: 3d tracking and forecasting with rich maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8748–8757, 2019.
  8. Unsupervised sampling promoting for stochastic human trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17874–17884, 2023a.
  9. Traj-mae: Masked autoencoders for trajectory prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8351–8362, 2023b.
  10. Towards efficient human-robot collaboration with robust plan recognition and trajectory prediction. IEEE Robotics and Automation Letters, 5(2):2602–2609, 2020.
  11. Adamsformer for spatial action localization in the future. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17885–17895, 2023.
  12. Goal-driven self-attentive recurrent networks for trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2518–2527, 2022.
  13. Universal transformers. In Proceedings of the International Conference on Learning Representations, 2019.
  14. Discriminative unsupervised feature learning with convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems, 2014.
  15. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations, 2018.
  16. Transformer networks for trajectory forecasting. In Proceedings of the IEEE International Conference on Pattern Recognition, pages 10335–10342, 2020.
  17. Vip3d: End-to-end visual trajectory prediction via 3d agent queries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5496–5506, 2023.
  18. Stochastic trajectory prediction via motion indeterminacy diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17113–17122, 2022.
  19. Social GAN: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2255–2264, 2018.
  20. Michael Hahn. Theoretical limitations of self-attention in neural sequence models. Transactions of the Association for Computational Linguistics, 8:156–171, 2020.
  21. Importance is in your attention: agent importance prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2532–2535, 2022.
  22. Long short-term memory. Neural Computation, 9(8):1735–1780, 1997.
  23. Planning-oriented autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17853–17862, 2023.
  24. Online adaptive temporal memory with certainty estimation for human trajectory prediction. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pages 940–949, 2023.
  25. The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2375–2384, 2019.
  26. Expanding the deployment envelope of behavior prediction via adaptive meta-learning. In Proceedings of the IEEE International Conference on Robotics and Automation, 2023.
  27. Motiondiffuser: Controllable multi-agent motion prediction using diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9644–9653, 2023.
  28. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. In Proceedings of the Advances in Neural Information Processing Systems, pages 137–146, 2019.
  29. Solomon Kullback. Information theory and statistics. Courier Corporation, 1997.
  30. Scene informer: Anchor-based occlusion inference and trajectory prediction in partially observable environments. arXiv preprint arXiv:2309.13893, 2023.
  31. Crowds by example. In Computer graphics forum, pages 655–664. Wiley Online Library, 2007.
  32. Conditional generative neural system for probabilistic trajectory prediction. In Proceedings of the International Conference on Intelligent Robots and Systems, 2019.
  33. Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2231–2241, 2022.
  34. Bcdiff: Bidirectional consistent diffusion for instantaneous trajectory prediction. In Proceedings of the Advances in Neural Information Processing Systems, 2023.
  35. Ttt++: When does self-supervised test-time training fail or thrive? In Proceedings of the Advances in Neural Information Processing Systems, pages 21808–21820, 2021.
  36. Stpotr: Simultaneous human trajectory and pose prediction using a non-autoregressive transformer for robot follow-ahead. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 9959–9965, 2023.
  37. It is not the journey but the destination: Endpoint conditioned trajectory prediction. In Proceedings of the European Conference on Computer Vision, pages 759–776, 2020.
  38. Leapfrog diffusion model for stochastic trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5517–5526, 2023.
  39. Social-STGCNN: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14424–14432, 2020.
  40. How many observations are enough? Knowledge distillation for trajectory forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6553–6562, 2022.
  41. Number sequence prediction problems for evaluating computational powers of neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 4626–4633, 2019.
  42. I-bert: Inductive generalization of transformer to arbitrary context lengths. arXiv preprint arXiv:2006.10220, 2020.
  43. Unsupervised learning of visual representations by solving jigsaw puzzles. In Proceedings of the European Conference on Computer Vision, pages 69–84. Springer, 2016.
  44. Leveraging future relationship reasoning for vehicle trajectory prediction. In Proceedings of the International Conference on Learning Representations, 2023.
  45. Improving transferability for cross-domain trajectory prediction via neural stochastic differential equation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
  46. You’ll never walk alone: Modeling social behavior for multi-target tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 261–268, 2009.
  47. Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic interaction graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13745–13755, 2023.
  48. SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1349–1358, 2019.
  49. Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In Proceedings of the European Conference on Computer Vision, pages 683–700, 2020.
  50. Motionlm: Multi-agent motion forecasting as language modeling. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8579–8590, 2023.
  51. Sgcn: Sparse graph convolution network for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8994–9003, 2021.
  52. Trajectory unified transformer for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9675–9684, 2023.
  53. Recursive social behavior graph for trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 660–669, 2020a.
  54. Human trajectory prediction with momentary observation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6467–6476, 2022.
  55. Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2446–2454, 2020b.
  56. Social-ssl: Self-supervised cross-sequence representation learning based on transformers for multi-agent trajectory prediction. In Proceedings of the European Conference on Computer Vision, pages 234–250, 2022.
  57. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, pages 5998–6008, 2017.
  58. Tent: Fully test-time adaptation by entropy minimization. In Proceedings of the International Conference on Learning Representations, 2021.
  59. Ltp: Lane-based trajectory prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17134–17142, 2022.
  60. Enhancing mapless trajectory prediction through knowledge distillation. arXiv preprint arXiv:2306.14177, 2023.
  61. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.
  62. Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6498–6507, 2022a.
  63. Remember intentions: Retrospective-memory-based trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6488–6497, 2022b.
  64. Socialvae: Human trajectory prediction using timewise latents. In Proceedings of the European Conference on Computer Vision, 2022c.
  65. Cf-lstm: Cascaded feature-based long short-term networks for predicting pedestrian trajectory. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12541–12548, 2020.
  66. Tra2tra: Trajectory-to-trajectory prediction with a global social spatial-temporal attentive neural network. IEEE Robotics and Automation Letters, 6(2):1574–1581, 2021.
  67. Adaptive trajectory prediction via transferable gnn. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6520–6531, 2022d.
  68. Uncovering the missing pattern: Unified framework towards trajectory imputation and prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9632–9643, 2023a.
  69. Human-centric scene understanding for 3d large-scale scenarios. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20349–20359, 2023b.
  70. Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In Proceedings of the European Conference on Computer Vision, pages 507–523, 2020.
  71. Universally slimmable networks and improved training techniques. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1803–1811, 2019.
  72. Slimmable neural networks. In Proceedings of the International Conference on Learning Representations, 2019.
  73. Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9813–9823, 2021.
  74. Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps. arXiv preprint arXiv:1910.03088, 2019.
  75. Hivt: Hierarchical vector transformer for multi-agent motion prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8823–8833, 2022.
  76. Query-centric trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17863–17873, 2023.
  77. Simultaneous past and current social interaction-aware trajectory prediction for multiple intelligent agents in dynamic scenes. ACM Transactions on Intelligent Systems and Technology, 13(1):1–16, 2021.
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Authors (2)

  1. Yi Xu
  2. Yun Fu