1. bookVolumen 24 (2023): Edición 1 (February 2023)
Detalles de la revista
License
Formato
Revista
eISSN
1407-6179
Primera edición
20 Mar 2000
Calendario de la edición
4 veces al año
Idiomas
Inglés
Acceso abierto

Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users

Publicado en línea: 28 Feb 2023
Volumen & Edición: Volumen 24 (2023) - Edición 1 (February 2023)
Páginas: 55 - 64
Detalles de la revista
License
Formato
Revista
eISSN
1407-6179
Primera edición
20 Mar 2000
Calendario de la edición
4 veces al año
Idiomas
Inglés

Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S. (2016) Social lstm: Human trajectory prediction in crowded spaces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 961–971.10.1109/CVPR.2016.110 Search in Google Scholar

Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., Eckstein, L. (2020) The ind dataset: A drone dataset of naturalistic road user trajectories at german intersections. In: 2020 IEEE Intelligent Vehicles Symposium (IV), 1929–1934. https://doi.org/10.1109/IV47402.2020.9304839.10.1109/IV47402.2020.9304839 Search in Google Scholar

Cheng, H., Liao, W., Tang, X., Yang, M.Y., Sester, M., Rosenhahn, B. (2021) Exploring dynamic context for multi-path trajectory prediction. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), 12795–12801. https://doi.org/10.1109/ICRA48506.2021.9562034.10.1109/ICRA48506.2021.9562034 Search in Google Scholar

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. https://doi.org/10.48550/ARXIV.2010.11929, https://arxiv.org/abs/2010.11929. Search in Google Scholar

Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A. (2018) Social gan: Socially acceptable trajectories with generative adversarial networks. https://doi.org/10.48550/ARXIV.1803.10892, https://arxiv.org/abs/1803.10892. Search in Google Scholar

Hochreiter, S., Schmidhuber, J. (1997) Long short-term memory. Neural computation, 9, 1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.10.1162/neco.1997.9.8.17359377276 Search in Google Scholar

Ivanovic, B., Pavone, M. (2018) The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. https://doi.org/10.48550/ARXIV.1810.05993, https://arxiv.org/abs/1810.05993. Search in Google Scholar

Jang, E., Gu, S., Poole, B. (2016) Categorical reparameterization with gumbel-softmax. https://doi.org/10.48550/ARXIV.1611.01144, https://arxiv.org/abs/1611.01144. Search in Google Scholar

Jiang, Z., Zheng, Y., Tan, H., Tang, B., Zhou, H. (2016) Variational deep embedding: A generative approach to clustering. CoRR abs/1611.05148, http://arxiv.org/abs/1611.05148. Search in Google Scholar

Kingma, D.P., Ba, J. (2014) Adam: A method for stochastic optimization. In: 3rd International Conference for Learning Representations, San Diego, https://doi.org/10.48550/ARXIV.1412.6980 https://arxiv.org/abs/1412.6980 Search in Google Scholar

Kingma, D.P., Welling, M. (2013) Auto-encoding variational bayes. https://doi.org/10.48550/ARXIV.1312.6114, https://arxiv.org/abs/1312.6114 Search in Google Scholar

Krajewski, R., Moers, T., Bock, J., Vater, L., Eckstein, L. (2020) The round dataset: A drone dataset of road user trajectories at roundabouts in germany. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1–6. https://doi.org/10.1109/ITSC45102.2020.9294728.10.1109/ITSC45102.2020.9294728 Search in Google Scholar

Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H.S., Chandraker, M. (2017) Desire: Distant future prediction in dynamic scenes with interacting agents. https://doi.org/10.48550/ARXIV.1704.04394, https://arxiv.org/abs/1704.04394. Search in Google Scholar

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B. (2021) Swin transformer: Hierarchical vision transformer using shifted windows. https://doi.org/10.48550/ARXIV.2103.14030, https://arxiv.org/abs/2103.14030. Search in Google Scholar

Maddison, C.J., Mnih, A., Teh, Y.W. (2016) The concrete distribution: A continuous relaxation of discrete random variables. https://doi.org/10.48550/ARXIV.1611.00712, https://arxiv.org/abs/1611.00712 Search in Google Scholar

Schmidt, S., Assmann, T., Junge, L., H öfer, M., Kastner, K., Manoeva, D., Matthies, E., Riestock, M., Rolof, S., Sass, S., Schmidt, M., Seidel, M., Weißflog, J. (2021) Shared autonomous cargo bike fleets-approaches for a novel sustainable urban mobility solution.10.46720/F2021-ACM-124 Search in Google Scholar

Sohn, K., Lee, H., Yan, X. (2015) Learning structured output representation using deep conditional generative models. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, 28. Curran Associates, Inc., https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf AI-BASED TRAJECTORY FORECASTS 15. Search in Google Scholar

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I. (2017) Attention is all you need. https://doi.org/10.48550/ARXIV.1706.03762, https://arxiv.org/abs/1706.03762. Search in Google Scholar

Xie, J., Girshick, R., Farhadi, A. (2015) Unsupervised deep embedding for clustering analysis. https://doi.org/10.48550/ARXIV.1511.06335, https://arxiv.org/abs/1511.06335 Search in Google Scholar

Yao, Y., Atkins, E., Johnson-Roberson, M., Vasudevan, R., Du, X. (2020) Bi-trap: Bi-directional pedestrian trajectory prediction with multi-modal goal estimation. https://doi.org/10.48550/ARXIV.2007.14558 https://arxiv.org/abs/2007.1455 Search in Google Scholar

Artículos recomendados de Trend MD