Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Start single access now
Access for companies
12th International Engine Congress 2025 | 25. + 26. February 2025

Meeting Place for the Powertrain and Sustainable Fuels Community

The congress will examine the future role of the internal combustion engine and its non-fossil fuels from a global perspective with leading experts. Be part of it! More information and booking options are available on our website.
EXTENDED SEARCH
Log in
Top
Published in:
Read chapter Read first chapter

2025 | OriginalPaper | Chapter

UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction

Authors : Lan Feng, Mohammadhossein Bahari, Kaouther Messaoud Ben Amor, Éloi Zablocki, Matthieu Cord, Alexandre Alahi

Published in: Computer Vision – ECCV 2024

Publisher: Springer Nature Switzerland

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored. While these questions can be studied by employing multiple datasets, it is challenging due to several discrepancies, e.g., in data formats, map resolution, and semantic annotation types. To address these challenges, we introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria, presenting new opportunities for the vehicle trajectory prediction field. In particular, using UniTraj, we conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. However, enlarging data size and diversity can substantially improve performance, leading to a new state-of-the-art result for the nuScenes dataset. We provide insights into dataset characteristics to explain these findings. The code can be found here: https://​github.​com/​vita-epfl/​UniTraj.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter ControlLLM: Augment Language Models with Tools by Searching on Graphs
next chapter DreamDissector: Learning Disentangled Text-to-3D Generation from 2D Diffusion Priors
Appendix
Available only for authorised users
Literature
1.
Amirian, J., Zhang, B., Castro, F.V., Baldelomar, J.J., Hayet, J.B., Pettré, J.: OpenTraj: assessing prediction complexity in human trajectories datasets. In: Proceedings of the Asian Conference on Computer Vision (2020)
2.
Bahari, M., Nejjar, I., Alahi, A.: Injecting knowledge in data-driven vehicle trajectory predictors. Transp. Res. Part C: Emerg. Technol. 128, 103010 (2021)CrossRef
3.
Bahari, M., et al.: Vehicle trajectory prediction works, but not everywhere. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17123–17133 (2022)
4.
Ben-Younes, H., Zablocki, É., Chen, M., Pérez, P., Cord, M.: Raising context awareness in motion forecasting. In: CVPRW, pp. 4408–4417. IEEE (2022)
5.
Bhattacharyya, P., Huang, C., Czarnecki, K.: SSL-lanes: self-supervised learning for motion forecasting in autonomous driving. In: Conference on Robot Learning, pp. 1793–1805. PMLR (2023)
6.
Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., Eckstein, L.: The inD dataset: a drone dataset of naturalistic road user trajectories at German intersections. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1929–1934. IEEE (2020)
7.
Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020)
8.
9.
10.
Chang, M.-F., et al.: Argoverse: 3D tracking and forecasting with rich maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
11.
Chen, G., Li, J., Lu, J., Zhou, J.: Human trajectory prediction via counterfactual analysis. In: ICCV, pp. 9804–9813. IEEE (2021)
12.
Chen, J., Wang, Z., Wang, J., Cai, B.: Q-eanet: implicit social modeling for trajectory prediction via experience-anchored queries. IET Intell. Transp. Syst. (2023)
13.
Coscia, P., Castaldo, F., Palmieri, F.A.N., Alahi, A., Savarese, S., Ballan, L.: Long-term path prediction in urban scenarios using circular distributions. J. Image Vis. Comput. 69, 81–91 (2018)CrossRef
14.
Ettinger, S., et al.: Large scale interactive motion forecasting for autonomous driving: the waymo open motion dataset. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9710–9719 (2021)
15.
Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: Uncertainty estimation for cross-dataset performance in trajectory prediction. In: IEEE International Conference on Robotics and Automation Workshop on Fresh Perspectives on the Future of Autonomous Driving (2022)
16.
Girgis, R., et al.: Latent variable sequential set transformers for joint multi-agent motion prediction. In: International Conference on Learning Representations (2022)
17.
Houston, J., et al.: One thousand and one hours: self-driving motion prediction dataset. In: Conference on Robot Learning, pp. 409–418. PMLR (2021)
18.
Hsu, K.C., Leung, K., Chen, Y., Fisac, J.F., Pavone, M.: Interpretable trajectory prediction for autonomous vehicles viacounterfactual responsibility. In: IEEE/RSJ International Conference on Intelligent Robots & Systems (2023)
19.
Ivanovic, B., Song, G., Gilitschenski, I., Pavone, M.: Trajdata: a unified interface to multiple human trajectory datasets. In: Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, New Orleans, USA (2023)
20.
Jiang, C., et al.: Motiondiffuser: controllable multi-agent motion prediction using diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9644–9653 (2023)
21.
Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)
22.
23.
Kothari, P., Kreiss, S., Alahi, A.: Human trajectory forecasting in crowds: a deep learning perspective. IEEE Trans. Intell. Transp. Syst. (2021)
24.
Kothari, P., Li, D., Liu, Y., Alahi, A.: Motion style transfer: modular low-rank adaptation for deep motion forecasting. In: CoRL. Proceedings of Machine Learning Research, vol. 205, pp. 774–784. PMLR (2022)
25.
Li, Q., et al.: Scenarionet: open-source platform for large-scale traffic scenario simulation and modeling. In: Advances in Neural Information Processing Systems (2023)
26.
27.
Liu, Y., Cadei, R., Schweizer, J., Bahmani, S., Alahi, A.: Towards robust and adaptive motion forecasting: a causal representation perspective. In: CVPR, pp. 17060–17071. IEEE (2022)
28.
Makansi, O., Çiçek, Ö., Marrakchi, Y., Brox, T.: On exposing the challenging long tail in future prediction of traffic actors. In: ICCV, pp. 13127–13137. IEEE (2021)
29.
Malinin, A., et al.: Shifts: a dataset of real distributional shift across multiple large-scale tasks. arXiv preprint arXiv:​2107.​07455 (2021)
30.
Messaoud, K., Yahiaoui, I., Verroust-Blondet, A., Nashashibi, F.: Attention based vehicle trajectory prediction. IEEE Trans. Intell. Veh. 6(1), 175–185 (2021)CrossRef
31.
Nayakanti, N., Al-Rfou, R., Zhou, A., Goel, K., Refaat, K.S., Sapp, B.: Wayformer: motion forecasting via simple & efficient attention networks. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2980–2987. IEEE (2023)
32.
Pourkeshavarz, M., Chen, C., Rasouli, A.: Learn tarot with mentor: a meta-learned self-supervised approach for trajectory prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8384–8393 (2023)
33.
Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory prediction in crowded scenes. In: European Conference on Computer Vision (ECCV), vol. 2, p. 5 (2016)
34.
Rudenko, A., Palmieri, L., Huang, W., Lilienthal, A.J., Arras, K.O.: The atlas benchmark: an automated evaluation framework for human motion prediction. In: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 636–643. IEEE (2022)
35.
Saadatnejad, S., Bahari, M., Khorsandi, P., Saneian, M., Moosavi-Dezfooli, S.M., Alahi, A.: Are socially-aware trajectory prediction models really socially-aware? arXiv preprint arXiv:​2108.​10879 (2021)
36.
Saadatnejad, S., et al.: Toward reliable human pose forecasting with uncertainty. IEEE Robot. Autom. Lett. 9(5), 4447–4454 (2024)CrossRef
37.
Sadeghian, A., Kosaraju, V., Gupta, A., Savarese, S., Alahi, A.: Towards a benchmark for human trajectory prediction. arXiv preprint, Trajnet (2018)
38.
Sarva, J., Wang, J., Tu, J., Xiong, Y., Manivasagam, S., Urtasun, R.: Adv3D: generating safety-critical 3D objects through closed-loop simulation. CoRR, abs/2311.01446 (2023)
39.
Schäfer, M., Zhao, K., Kummert, A.: Caspnet++: joint multi-agent motion prediction (2023)
40.
Shao, W., Xu, Y., Li, J., Lv, C., Wang, W., Wang, H.: How does traffic environment quantitatively affect the autonomous driving prediction? IEEE Trans. Intell. Transp. Syst. (2023)
41.
42.
Shi, S., Jiang, L., Dai, D., Schiele, B.: Motion transformer with global intention localization and local movement refinement. In: Advances in Neural Information Processing Systems, vol. 35, pp. 6531–6543 (2022)
43.
Varadarajan, B., et al.: Multipath++: efficient information fusion and trajectory aggregation for behavior prediction. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 7814–7821. IEEE (2022)
44.
Wang, Y., Zhang, P., Bai, L., Xue, J.: FEND: a future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction. In: CVPR, pp. 1400–1409. IEEE (2023)
45.
Weng, X., Ivanovic, B., Kitani, K., Pavone, M.: Whose track is it anyway? Improving robustness to tracking errors with affinity-based trajectory prediction. In: CVPR, pp. 6563–6572. IEEE (2022)
46.
Wilson, B., et al. Argoverse 2: next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:​2301.​00493 (2023)
47.
Xu, Y., et al.: Towards motion forecasting with real-world perception inputs: are end-to-end approaches competitive? In: ICRA (2024)
48.
Yao, Z., Li, X., Lang, B., Chuah, M.C.: Goal-LBP: goal-based local behavior guided trajectory prediction for autonomous driving. IEEE Trans. Intell. Transp. Syst. 1–10 (2023)
49.
Ye, L., Zhou, Z., Wang, J.: Improving the generalizability of trajectory prediction models with frenet-based domain normalization. In: IEEE International Conference on Robotics and Automation (ICRA) (2023)
50.
Zhan, W., et al.: Interaction dataset: an international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps. arXiv preprint arXiv:​1910.​03088 (2019)
51.
Zhang, P., Bai, L., Xue, J., Fang, J., Zheng, N., Ouyang, W.: Trajectory forecasting from detection with uncertainty-aware motion encoding. CoRR, abs/2202.01478 (2022)
52.
Zhou, X., Koltun, V., Krähenbühl, P.: Simple multi-dataset detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7571–7580 (2022)
53.
Zhou, Z., Wang, J., Li, Y.-H., Huang, Y.-K.: Query-centric trajectory prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17863–17873 (2023)
Metadata
Title
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
Authors
Lan Feng
Mohammadhossein Bahari
Kaouther Messaoud Ben Amor
Éloi Zablocki
Matthieu Cord
Alexandre Alahi
Copyright Year
2025
Book
Computer Vision – ECCV 2024

Print ISBN: 978-3-031-73253-9

Electronic ISBN: 978-3-031-73254-6

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-73254-6_7

Premium Partner

    Image Credits