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In self-driving, predicting future in terms of location and motion of all the agents around the vehicle is a crucial requirement for planning.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Proc. of the International Conf. on Learning Representations (ICLR) (2014)
2014
Earlier work this paper cites.
Karl, M., Soelch, M., Bayer, J., van der Smagt, P.: Deep variational bayes filters: Unsupervised learning of state space models from raw data. In: Proc. of the International Conf. on Learning Representations (ICLR) (2017)
2017
Earlier work this paper cites.
Krishnan, R.G., Shalit, U., Sontag, D.: Structured inference networks for nonlinear state space models. In: Proc. of the Conf. on Artificial Intelligence (AAAI) (2017)
2017
Earlier work this paper cites.
Székely, G.J., Rizzo, M.L.: The energy of data. Annual Review of Statistics and Its Application 4
2017
Earlier work this paper cites.
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R.H., Levine, S.: Stochastic variational video prediction. In: Proc. of the International Conf. on Learning Representations (ICLR) (2018)
2018
Earlier work this paper cites.
Casas, S., Luo, W., Urtasun, R.: Intentnet: Learning to predict intention from raw sensor data. In: Proc. Conf. on Robot Learning (CoRL) (2018)
2018
Earlier work this paper cites.
Chen, R.T., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)
2018
Earlier work this paper cites.
Denton, E., Fergus, R.: Stochastic video generation with a learned prior. In: Proc. of the International Conf. on Machine learning (ICML) (2018)
2018
Earlier work this paper cites.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Earlier work this paper cites.
Luo, W., Yang, B., Urtasun, R.: Fast and furious: Real time end-to-end 3D detection, tracking and motion forecasting with a single convolutional net. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Earlier work this paper cites.
Castrejon, L., Ballas, N., Courville, A.: Improved conditional vrnns for video prediction. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
Earlier work this paper cites.
Chang, M.F., Lambert, J.W., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., Hays, J.: Argoverse: 3d tracking and forecasting with rich maps. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Earlier work this paper cites.
Gregor, K., Besse, F.: Temporal difference variational auto-encoder. In: Proc. of the International Conf. on Learning Representations (ICLR) (2019)
2019
Earlier work this paper cites.
Hong, J., Sapp, B., Philbin, J.: Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Cited alongside, same era.
Minderer, M., Sun, C., Villegas, R., Cole, F., Murphy, K.P., Lee, H.: Unsupervised learning of object structure and dynamics from videos. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Cited alongside, same era.
Rubanova, Y., Chen, R.T.Q., Duvenaud, D.K.: Latent ordinary differential equations for irregularly-sampled time series. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Cited alongside, same era.
Tang, Y.C., Salakhutdinov, R.: Multiple futures prediction. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Cited alongside, same era.
Hu, A., Cotter, F., Mohan, N., Gurau, C., Kendall, A.: Probabilistic future prediction for video scene understanding. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
2020
Later among the works it cites.
Huang, X., McGill, S.G., DeCastro, J.A., Fletcher, L., Leonard, J.J., Williams, B.C., Rosman, G.: Diversitygan: Diversity-aware vehicle motion prediction via latent semantic sampling. IEEE Robotics and Automation Letters (RA-L) (2020)
2020
Later among the works it cites.
Liang, M., Yang, B., Hu, R., Chen, Y., Liao, R., Feng, S., Urtasun, R.: Learning lane graph representations for motion forecasting. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
2020
Later among the works it cites.
Philion, J., Fidler, S.: Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
2020
Later among the works it cites.
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Villegas, R., Pathak, A., Kannan, H., Erhan, D., Le, Q.V., Lee, H.: High fidelity video prediction with large stochastic recurrent neural networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Cited alongside, same era.
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Casas, S., Gulino, C., Liao, R., Urtasun, R.: Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data. In: Proc. IEEE International Conf. on Robotics and Automation (ICRA) (2020)
2020
Cited alongside, same era.
Chai, Y., Sapp, B., Bansal, M., Anguelov, D.: Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In: Proc. Conf. on Robot Learning (CoRL) (2020)
2020
Cited alongside, same era.
Cheng, B., Collins, M.D., Zhu, Y., Liu, T., Huang, T.S., Adam, H., Chen, L.C.: Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. In: CVPR (2020)
2020
Cited alongside, same era.
Franceschi, J.Y., Delasalles, E., Chen, M., Lamprier, S., Gallinari, P.: Stochastic latent residual video prediction. In: Proc. of the International Conf. on Machine learning (ICML) (2020)
2020
Cited alongside, same era.
Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., Schmid, C.: Vectornet: Encoding HD maps and agent dynamics from vectorized representation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Hendy, N., Sloan, C., Tian, F., Duan, P., Charchut, N., Xie, Y., Wang, C., Philbin, J.: FISHING net: Future inference of semantic heatmaps in grids. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)
2020
Cited alongside, same era.
Sriram, N.N., Liu, B., Pittaluga, F., Chandraker, M.: SMART: Simultaneous multi-agent recurrent trajectory prediction. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
2020
Later among the works it cites.
Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B., Shen, Y., Shen, Y., Chai, Y., Schmid, C., Li, C., Anguelov, D.: TNT: target-driven trajectory prediction. In: Proc. Conf. on Robot Learning (CoRL) (2020)
2020
Later among the works it cites.
Akan, A.K., Erdem, E., Erdem, A., Güney, F.: Slamp: Stochastic latent appearance and motion prediction. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2021)
2021
Later among the works it cites.
Djuric, N., Cui, H., Su, Z., Wu, S., Wang, H., Chou, F., Martin, L.S., Feng, S., Hu, R., Xu, Y., Dayan, A., Zhang, S., Becker, B.C., Meyer, G.P., Vallespi-Gonzalez, C., Wellington, C.K.: Multixnet: Multiclass multistage multimodal motion prediction. In: Proc. IEEE Intelligent Vehicles Symposium (IV) (2021)
2021
Later among the works it cites.
Gu, J., Sun, C., Zhao, H.: Densetnt: End-to-end trajectory prediction from dense goal sets. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2021)
2021
Later among the works it cites.
Hu, A., Murez, Z., Mohan, N., Dudas, S., Hawke, J., Badrinarayanan, V., Cipolla, R., Kendall, A.: FIERY: Future instance segmentation in bird’s-eye view from surround monocular cameras. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2021)
2021
Later among the works it cites.
2022
Closest in time.
Murphy, K.P.: Probabilistic Machine Learning: Advanced Topics. MIT Press (2023), probml.ai
2023
Closest in time.