Abstract
Predicting pedestrian trajectories is vital for improving safety and efficiency in human-robot interaction within traffic systems. However, this task is inherently challenging due to the unpredictable nature of human behavior. We present MotDiff, a method based on Variational Auto-encoders with a diffusion prior, which synthesizes latent variables to capture the unobserved uncertainty and complex relation among agents. We provide a comprehensive theoretical background of our approach and evaluate it with various generative modeling methods using three public pedestrian datasets, showing its effectiveness in achieving both accuracy and diversity.
L. Qiu—Work partly done during internship at Cognitive Computing Lab, Baidu Research.
J. Yan—The SJTU authors were supported by NSFC (61972250, U19B2035), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amirian, J., Hayet, J.B., Pettré, J.: Social ways: Learning multi-modal distributions of pedestrian trajectories with GANs. In: CVPR Workshops (2019)
Dendorfer, P., Elflein, S., Leal-Taixé, L.: Mg-gan: A multi-generator model preventing out-of-distribution samples in pedestrian trajectory prediction. In: ICCV (2021)
Dendorfer, P., Osep, A., Leal-Taixé, L.: Goal-GAN: multimodal trajectory prediction based on goal position estimation. In: ACCV (2020)
Girgis, R., et al.: Latent variable sequential set transformers for joint multi-agent motion prediction. arXiv preprint arXiv:2104.00563 (2021)
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: CVPR, pp. 2255–2264 (2018)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
Janner, M., Du, Y., Tenenbaum, J.B., Levine, S.: Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991 (2022)
Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., Chandraker, M.: Desire: distant future prediction in dynamic scenes with interacting agents. In: CVPR, pp. 336–345 (2017)
Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer graphics forum, vol. 26, pp. 655–664. Wiley Online Library (2007)
Mangalam, K., et al.: It is not the journey but the destination: Endpoint conditioned trajectory prediction. In: ECCV, pp. 759–776 (2020)
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)
Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: ECCV. pp. 452–465 (2010)
Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory understanding in crowded scenes. In: ECCV, pp. 549–565 (2016)
Sadeghian, A., Kosaraju, V., Gupta, A., Savarese, S., Alahi, A.: TrajNet: towards a benchmark for human trajectory prediction. arXiv preprint (2018)
Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., Savarese, S.: Sophie: an attentive gan for predicting paths compliant to social and physical constraints. In: CVPR, pp. 1349–1358 (2019)
Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data. In: ECCV, pp. 683–700 (2020)
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)
Tomczak, J., Welling, M.: VAE with a vampprior. In: AISTATS (2018)
Vahdat, A., Andriyash, E., Macready, W.: Dvae#: Discrete variational autoencoders with relaxed boltzmann priors. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30 (2017)
Wehenkel, A., Louppe, G.: Diffusion priors in variational autoencoders. arXiv preprint arXiv:2106.15671 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Qiu, L., Li, X., Sun, M., Yan, J. (2024). Conditional Variational Inference for Multi-modal Trajectory Prediction with Latent Diffusion Prior. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-7019-3_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7018-6
Online ISBN: 978-981-99-7019-3
eBook Packages: Computer ScienceComputer Science (R0)