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For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
A flexible new technique for camera calibration
Zhang, Z · 2000
Earlier work this paper cites.
Sensitivity analysis for importance assessment
Saltelli, A · 2002
Earlier work this paper cites.
Blind super-resolution using a learning-based approach
Bégin, I. and Ferrie, F · 2004
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
Earlier work this paper cites.
Global sensitivity analysis: the primer
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S · 2008
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Zhang, Y. and Wallace, B · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Santana, E. and Hotz, G · 2016
Earlier work this paper cites.
Examining the impact of blur on recognition by convolutional networks
Vasiljevic, I., Chakrabarti, A., and Shakhnarovich, G · 2016
Earlier work this paper cites.
A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S. and Karam, L · 2017
Earlier work this paper cites.
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
1 Year, 1000km: The Oxford RobotCar Dataset
Maddern, W., Pascoe, G., Linegar, C., and Newman, P · 2017
Cited alongside, same era.
A bayesian data augmentation approach for learning deep models
Tran, T., Pham, T., Carneiro, G., Palmer, L., and Reid, I · 2017
Cited alongside, same era.
The raincouver scene parsing benchmark for self-driving in adverse weather and at night
Tung, F., Chen, J., Meng, L., and Little, J. J · 2017
Cited alongside, same era.
On classification of distorted images with deep convolutional neural networks
Zhou, Y., Song, S., and Cheung, N.-M · 2017
Cited alongside, same era.
A collection of labeled car driving datasets, https://github.com/sullychen/driving-datasets, 2018
Chen, S · 2018
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
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ADAPS: Autonomous driving via principled simulations
Li, W., Wolinski, D., and Lin, M. C · 2019
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Adversarial training for free!
Shafahi, A., Najibi, M., Ghiasi, M. A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
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Ford multi-av seasonal dataset
Agarwal, S., Vora, A., Pandey, G., Williams, W., Kourous, H., and McBride, J · 2020
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Yolov4: Optimal speed and accuracy of object detection
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M · 2020
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Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
Cited alongside, same era.
Robustness of deep convolutional neural networks for image degradations
Ghosh, S., Shet, R., Amon, P., Hutter, A., and Kaup, A · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning
Ramanishka, V., Chen, Y.-T., Misu, T., and Saenko, K · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
Cited alongside, same era.
Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Tian, Y., Pei, K., Jana, S., and Ray, B · 2018
Cited alongside, same era.
Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems
Zhang, M., Zhang, Y., Zhang, L., Liu, C., and Khurshid, S · 2018
Cited alongside, same era.
Fuzz testing based data augmentation to improve robustness of deep neural networks
Gao, X., Saha, R. K., Prasad, M. R., and Roychoudhury, A · 2020
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A2D2: Audi Autonomous Driving Dataset
Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A. S., Hauswald, L., Pham, V. H., Mühlegg, M., Dorn, S., Fernandez, T., Jänicke, M., Mirashi, S., Savani, C., Sturm, M., Vorobiov, O., Oelker, M., Garreis, S., and Schuberth, P · 2020
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MaxUp: A Simple Way to Improve Generalization of Neural Network Training
Gong, C., Ren, T., Ye, M., and Liu, Q · 2020
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2020
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Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space
Pestana, C., Akhtar, N., Liu, W., Glance, D., and Mian, A · 2020
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Canadian adverse driving conditions dataset
Pitropov, M., Garcia, D., Rebello, J., Smart, M., Wang, C., Czarnecki, K., and Waslander, S · 2020
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Preparing for the worst: Making networks less brittle with adversarial batch normalization
Shu, M., Wu, Z., Goldblum, M., and Goldstein, T · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al · 2020
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Recognizing instagram filtered images with feature de-stylization
Wu, Z., Wu, Z., Singh, B., and Davis, L · 2020
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Adversarial examples improve image recognition
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A. L., and Le, Q. V · 2020
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