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The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the driving behaviour from experts.
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Rezende, D. J. and Mohamed, S · 2015
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End to end learning for self-driving cars
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CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V · 2017
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Desire: Distant future prediction in dynamic scenes with interacting agents
Lee, N., Choi, W., Vernaza, P., Choy, C. B., Torr, P. H., and Chandraker, M · 2017
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Agile autonomous driving using end-to-end deep imitation learning
Pan, Y., Cheng, C.-A., Saigol, K., Lee, K., Yan, X., Theodorou, E., and Boots, B · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Learning-based model predictive control for autonomous racing
Kabzan, J., Hewing, L., Liniger, A., and Zeilinger, M. N · 2019
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Precog: Prediction conditioned on goals in visual multi-agent settings
Rhinehart, N., McAllister, R., Kitani, K., and Levine, S · 2019
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Trajformer: Trajectory prediction with local self-attentive contexts for autonomous driving
Bhat, M., Francis, J., and Oh, J · 2020
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Learning to explore using active neural slam
Chaplot, D. S., Gandhi, D., Gupta, S., Gupta, A., and Salakhutdinov, R · 2020
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Learning a distributed control scheme for demand flexibility in thermostatically controlled loads
Chen, B., Yao, W., Francis, J., and Bergés, M · 2020
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Amos, B., Rodriguez, I. D. J., Sacks, J., Boots, B., and Kolter, J. Z · 2018
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Bansal, M., Krizhevsky, A., and Ogale, A · 2018
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End-to-end driving via conditional imitation learning
Codevilla, F., Müller, M., López, A., Koltun, V., and Dosovitskiy, A · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Orthographic feature transform for monocular 3d object detection
Roddick, T., Kendall, A., and Cipolla, R · 2018
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Conditional affordance learning for driving in urban environments
Sauer, A., Savinov, N., and Geiger, A · 2018
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Learning exploration policies for navigation
Chen, T., Gupta, S., and Gupta, A · 2019
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Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
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Learning situational driving
Ohn-Bar, E., Prakash, A., Behl, A., Chitta, K., and Geiger, A · 2020
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Diverse and admissible trajectory forecasting through multimodal context understanding
Park, S. H., Lee, G., Bhat, M., Seo, J., Kang, M., Francis, J., Jadhav, A. R., Liang, P. P., and Morency, L.-P · 2020
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Safe autonomous racing via approximate reachability on ego-vision, 2021
Chen, B., Francis, J., Oh, J., Nyberg, E., and Herbert, S. L · 2021
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Learn-to-race: A multimodal control environment for autonomous racing
Herman, J., Francis, J., Ganju, S., Chen, B., Koul, A., Gupta, A., Skabelkin, A., Zhukov, I., Kumskoy, M., and Nyberg, E · 2021
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Jacobian determinant of normalizing flows
Liao, H. and He, J · 2021
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2021
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Learn-to-race challenge 2022: Benchmarking safe learning and cross-domain generalisation in autonomous racing
Francis, J., Chen, B., Ganju, S., Kathpal, S., Poonganam, J., Shivani, A., Vyas, V., Genc, S., Zhukov, I., Kumskoy, M., Oh, J., Nyberg, E., and Herbert, S. L · 2022
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