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We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions.
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Máttyus, G., Wang, S., Fidler, S., Urtasun, R.: Hd maps: Fine-grained road segmentation by parsing ground and aerial images. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3611–3619 (2016)
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Cui, H., Radosavljevic, V., Chou, F.C., Lin, T.H., Nguyen, T., Huang, T.K., Schneider, J., Djuric, N.: Multimodal trajectory predictions for autonomous driving using deep convolutional networks. 2019 International Conference on Robotics and Automation (ICRA) pp. 2090–2096 (2018)
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Homayounfar, N., Ma, W.C., Lakshmikanth, S.K., Urtasun, R.: Hierarchical recurrent attention networks for structured online maps. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 3417–3426 (2018)
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Yang, B., Liang, M., Urtasun, R.: Hdnet: Exploiting hd maps for 3d object detection. In: Conference on Robot Learning. pp. 146–155 (2018)
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Chang, M.F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al.: Argoverse: 3d tracking and forecasting with rich maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8748–8757 (2019)
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Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., Urtasun, R.: End-to-end interpretable neural motion planner. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
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Casas, S., Gulino, C., Liao, R., Urtasun, R.: Spatially-aware graph neural networks for relational behavior forecasting from sensor data. In: ICRA (2020)
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Casas, S., Gulino, C., Suo, S., Luo, K., Liao, R., Urtasun, R.: Implicit latent variable model for scene-consistent motion forecasting. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
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Casas, S., Gulino, C., Suo, S., Urtasun, R.: The importance of prior knowledge in precise multimodal prediction. In: IROS (2020)
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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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11525–11533 (2020)
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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)
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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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11553–11562 (2020)
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Sadat, A., Casas, S., Ren, M., Wu, X., Dhawan, P., Urtasun, R.: Perceive, predict, and plan: Safe motion planning through interpretable semantic representations. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
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Zeng, W., Wang, S., Liao, R., Chen, Y., Yang, B., Urtasun, R.: Dsdnet: Deep structured self-driving network. In: ECCV (2020)
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