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In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network.
Pomerleau, D.A.: Alvinn: An autonomous land vehicle in a neural network. In: NeurIPS (1989)
1989
Earlier work this paper cites.
Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: An empirical study. In: Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence (1999)
1999
Earlier work this paper cites.
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations. Exploring artificial intelligence in the new millennium (2003)
2003
Earlier work this paper cites.
Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., et al.: Junior: The stanford entry in the urban challenge. Journal of field Robotics (2008)
2008
Earlier work this paper cites.
Ziebart, B.D., Maas, A.L., Bagnell, J.A., Dey, A.K.: Maximum entropy inverse reinforcement learning. In: AAAI (2008)
2008
Earlier work this paper cites.
Buehler, M., Iagnemma, K., Singh, S.: The DARPA urban challenge: autonomous vehicles in city traffic (2009)
2009
Earlier work this paper cites.
Ihler, A., McAllester, D.: Particle belief propagation. In: Artificial Intelligence and Statistics (2009)
2009
Earlier work this paper cites.
Sudderth, E.B., Ihler, A.T., Isard, M., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. Communications of the ACM (2010)
2010
Earlier work this paper cites.
Weiss, Y., Pearl, J.: Belief propagation: technical perspective. Communications of the ACM (2010)
2010
Earlier work this paper cites.
Yamaguchi, K., Hazan, T., McAllester, D., Urtasun, R.: Continuous markov random fields for robust stereo estimation. In: ECCV (2012)
2012
Earlier work this paper cites.
Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D.: Intention-aware motion planning. In: Algorithmic foundations of robotics X (2013)
2013
Earlier work this paper cites.
Hardy, J., Campbell, M.: Contingency planning over probabilistic obstacle predictions for autonomous road vehicles. IEEE Transactions on Robotics (2013)
2013
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv (2013)
2013
Earlier work this paper cites.
Yamaguchi, K., McAllester, D., Urtasun, R.: Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: ECCV (2014)
2014
Earlier work this paper cites.
Ziegler, J., Bender, P., Dang, T., Stiller, C.: Trajectory planning for bertha—a local, continuous method. In: Intelligent Vehicles Symposium Proceedings, 2014 IEEE (2014)
2014
Earlier work this paper cites.
Chen, L.C., Schwing, A., Yuille, A., Urtasun, R.: Learning deep structured models. In: ICML (2015)
2015
Earlier work this paper cites.
Schwing, A.G., Urtasun, R.: Fully connected deep structured networks. arXiv (2015)
2015
Earlier work this paper cites.
Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: NeurIPS (2015)
2015
Earlier work this paper cites.
Wulfmeier, M., Ondruska, P., Posner, I.: Maximum entropy deep inverse reinforcement learning. arXiv (2015)
2015
Earlier work this paper cites.
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social lstm: Human trajectory prediction in crowded spaces. In: CVPR (2016)
2016
Earlier work this paper cites.
Belanger, D., McCallum, A.: Structured prediction energy networks. In: ICML (2016)
2016
Earlier work this paper cites.
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., et al.: End to end learning for self-driving cars. arXiv (2016)
2016
Cited alongside, same era.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: ECCV (2016)
2016
Cited alongside, same era.
Zhai, S., Cheng, Y., Lu, W., Zhang, Z.: Deep structured energy based models for anomaly detection. In: ICML (2016)
2016
Cited alongside, same era.
Zhan, W., Liu, C., Chan, C.Y., Tomizuka, M.: A non-conservatively defensive strategy for urban autonomous driving. In: Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on (2016)
2016
Cited alongside, same era.
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. arXiv (2017)
Hong, J., Sapp, B., Philbin, J.: Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions. In: CVPR (2019)
2019
Later among the works it cites.
Jain, A., Casas, S., Liao, R., Xiong, Y., Feng, S., Segal, S., Urtasun, R.: Discrete residual flow for probabilistic pedestrian behavior prediction. arXiv (2019)
2019
Later among the works it cites.
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: Fast encoders for object detection from point clouds. In: CVPR (2019)
2019
Later among the works it cites.
Ma, W.C., Wang, S., Hu, R., Xiong, Y., Urtasun, R.: Deep rigid instance scene flow. In: CVPR. pp. 3614–3622 (2019)
2019
Later among the works it cites.
Min Choi, H., Kang, H., Hyun, Y.: Multi-view reprojection architecture for orientation estimation. In: ICCV (2019)
2019
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2017
Cited alongside, same era.
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 (2017)
2017
Cited alongside, same era.
Ma, W.C., Huang, D.A., Lee, N., Kitani, K.M.: Forecasting interactive dynamics of pedestrians with fictitious play. In: CVPR. pp. 774–782 (2017)
2017
Cited alongside, same era.
Casas, S., Luo, W., Urtasun, R.: Intentnet: Learning to predict intention from raw sensor data. In: Proceedings of The 2nd Conference on Robot Learning (2018)
2018
Cited alongside, same era.
Codevilla, F., Miiller, M., López, A., Koltun, V., Dosovitskiy, A.: End-to-end driving via conditional imitation learning. In: ICRA (2018)
2018
Cited alongside, same era.
Deo, N., Trivedi, M.M.: Convolutional social pooling for vehicle trajectory prediction. In: CVPR (2018)
2018
Cited alongside, same era.
Fan, H., Zhu, F., Liu, C., Zhang, L., Zhuang, L., Li, D., Zhu, W., Hu, J., Li, H., Kong, Q.: Baidu apollo em motion planner. arXiv (2018)
2018
Cited alongside, same era.
Graber, C., Meshi, O., Schwing, A.: Deep structured prediction with nonlinear output transformations. In: NeurIPS (2018)
2018
Cited alongside, same era.
Later among the works it cites.
Rhinehart, N., McAllister, R., Kitani, K., Levine, S.: Precog: Prediction conditioned on goals in visual multi-agent settings. arXiv (2019)
2019
Later among the works it cites.
Sadat, A., Ren, M., Pokrovsky, A., Lin, Y.C., Yumer, E., Urtasun, R.: Jointly learnable behavior and trajectory planning for self-driving vehicles. arXiv (2019)
2019
Later among the works it cites.
Tang, Y.C., Salakhutdinov, R.: Multiple futures prediction. arXiv (2019)
2019
Later among the works it cites.
Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., Urtasun, R.: End-to-end interpretable neural motion planner. In: CVPR (2019)
2019
Later among the works it cites.
Zhao, T., Xu, Y., Monfort, M., Choi, W., Baker, C., Zhao, Y., Wang, Y., Wu, Y.N.: Multi-agent tensor fusion for contextual trajectory prediction. In: CVPR (2019)
2019
Later among the works it cites.
Zhu, B., Jiang, Z., Zhou, X., Li, Z., Yu, G.: Class-balanced grouping and sampling for point cloud 3d object detection. arXiv (2019)
2019
Later among the works it cites.
Casas, S., Gulino, C., Suo, S., Luo, K., Liao, R., Urtasun, R.: Implicit latent variable model for scene-consistent motion forecasting. In: ECCV (2020)
2020
Closest in time.
Casas, S., Gulino, C., Suo, S., Urtasun, R.: The importance of prior knowledge in precise multimodal prediction. In: IROS (2020)
2020
Closest in time.
Li, L., Yang, B., Liang, M., Zeng, W., Ren, M., Segal, S., Urtasun, R.: End-to-end contextual perception and prediction with interaction transformer. In: IROS (2020)
2020
Closest in time.
Liang, M., Yang, B., Hu, R., Chen, Y., Liao, R., Feng, S., Urtasun, R.: Learning lane graph representations for motion forecasting. In: ECCV (2020)
2020
Closest in time.
Liang, M., Yang, B., Zeng, W., Chen, Y., Hu, R., Casas, S., Urtasun, R.: Pnpnet: End-to-end perception and prediction with tracking in the loop. In: CVPR (2020)
2020
Closest in time.
Manivasagam, S., Wang, S., Wong, K., Zeng, W., Sazanovich, M., Tan, S., Yang, B., Ma, W.C., Urtasun, R.: Lidarsim: Realistic lidar simulation by leveraging the real world. In: CVPR (2020)
2020
Closest in time.
Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., Wolff, E.M.: Covernet: Multimodal behavior prediction using trajectory sets. In: CVPR (2020)
2020
Closest in time.
Sadat, A., Casas, S., Ren, M., Wu, X., Dhawan, P., Urtasun, R.: Perceive, predict, and plan: Safe motion planning through interpretable semantic representations. In: ECCV (2020)
2020
Closest in time.
Wang, T.H., Manivasagam, S., Liang, M., Yang, B., Zeng, W., Raquel, U.: V2vnet: Vehicle-to-vehicle communication for joint perception and prediction. In: ECCV (2020)
2020
Closest in time.