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In this work we show that semi-supervised models for vehicle trajectory prediction significantly improve performance over supervised models on state-of-the-art real-world benchmarks.
End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., et al.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Going deeper: Autonomous steering with neural memory networks
Fernando, T., Denman, S., Sridharan, S., Fookes, C.: · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2017
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Convolutional social pooling for vehicle trajectory prediction
Deo, N., Trivedi, M.M.: · 2018
Earlier work this paper cites.
End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions
Yang, Z., Zhang, Y., Yu, J., Cai, J., Luo, J.: · 2018
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End-to-end learning of driving models with surround-view cameras and route planners
Hecker, S., Dai, D., Van Gool, L.: · 2018
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Winning the ICCV 2019 Learning to Drive Challenge
Diodato, M., Li, Y., Goyal, M., Drori, I.: · 2019
Earlier work this paper cites.
Unsupervised pre-training of image features on non-curated data
Caron, M., Bojanowski, P., Mairal, J., Joulin, A.: · 2019
Earlier work this paper cites.
Billion-scale semi-supervised learning for image classification
Yalniz, I.Z., Jégou, H., Chen, K., Paluri, M., Mahajan, D.: · 2019
Cited alongside, same era.
Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction
Chai, Y., Sapp, B., Bansal, M., Anguelov, D.: · 2019
Cited alongside, same era.
Multiple futures prediction
Tang, C., Salakhutdinov, R.R.: · 2019
Cited alongside, same era.
The Trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs
Ivanovic, B., Pavone, M.: · 2019
Cited alongside, same era.
Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions
Hong, J., Sapp, B., Philbin, J.: · 2019
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: · 2020
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Multi-head attention-based probabilistic vehicle trajectory prediction
Kim, H., Kim, D., Kim, G., Cho, J., Huh, K.: · 2020
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Spatially-aware graph neural networks for relational behavior forecasting from sensor data
Casas, S., Gulino, C., Liao, R., Urtasun, R.: · 2020
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The importance of prior knowledge in precise multimodal prediction
Casas, S., Gulino, C., Suo, S., Urtasun, R.: · 2020
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Bansal, M., Krizhevsky, A., Ogale, A.: · 2019
Cited alongside, same era.
Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation
Messaoud, K., Deo, N., Trivedi, M.M., Nashashibi, F.: · 2020
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CoverNet: Multimodal behavior prediction using trajectory sets
Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., Wolff, E.M.: · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: · 2020
Cited alongside, same era.
Trajectograms: Which semi-supervised trajectory prediction model to use?
Lamm, N., Srikanth, M., Jaiprakash, S., Drori, I.: · 2020
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nuScenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: · 2020
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Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving
Djuric, N., Radosavljevic, V., Cui, H., Nguyen, T., Chou, F., Lin, T., Singh, N., Schneider, J.: · 2093
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Multimodal trajectory predictions for autonomous driving using deep convolutional networks
Cui, H., Radosavljevic, V., Chou, F.C., Lin, T.H., Nguyen, T., Huang, T.K., Schneider, J., Djuric, N.: · 2096
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