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We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation.
Vpgnet: Vanishing point guided network for lane and road marking detection and recognition
Lee, S., Kim, J., Shin Yoon, J., Shin, S., Bailo, O., Kim, N., Lee, T.H., Seok Hong, H., Han, S.H., So Kweon, I.: · 1955
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Estimating the mean and variance of the target probability distribution
Nix, D.A., Weigend, A.S.: · 1994
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Estimating the mean and variance of the target probability distribution
Nix, D.A., Weigend, A.S.: · 1994
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
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Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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An empirical evaluation of deep learning on highway driving
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., Mujica, F.A., Coates, A., Ng, A.Y.: · 2015
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Deeplanes: End-to-end lane position estimation using deep neural networksa
Gurghian, A., Koduri, T., Bailur, S.V., Carey, K.J., Murali, V.N.: · 2016
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Uncertainty in deep learning
Gal, Y.: · 2016
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., Ghahramani, Z.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., Blundell, C.: · 2017
Cited alongside, same era.
Semantic instance segmentation with a discriminative loss function
Brabandere, B.D., Neven, D., Gool, L.V.: · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., Blundell, C.: · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: · 2017
Cited alongside, same era.
Towards end-to-end lane detection: an instance segmentation approach
A mixed classification-regression framework for 3d pose estimation from 2d images
Mahendran, S., Ali, H., Vidal, R.: · 2018
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Learning lightweight lane detection cnns by self attention distillation
Hou, Y., Ma, Z., Liu, C., Loy, C.C.: · 2019
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El-gan: Embedding loss driven generative adversarial networks for lane detection
Ghafoorian, M., Nugteren, C., Baka, N., Booij, O., Hofmann, M.: · 2019
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Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving
Choi, J., Chun, D., Kim, H., Lee, H.J.: · 2019
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Monoloco: Monocular 3d pedestrian localization and uncertainty estimation
Bertoni, L., Kreiss, S., Alahi, A.: · 2019
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Evaluating and calibrating uncertainty prediction in regression tasks
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Neven, D., Brabandere, B., Georgoulis, S., Proesmans, M., Van Gool, L.: · 2018
Cited alongside, same era.
Spatial as deep: Spatial cnn for traffic scene understanding
Xingang Pan, Jianping Shi, P.L.X.W., Tang, X.: · 2018
Cited alongside, same era.
Deep multi-sensor lane detection
Bai, M., Mattyus, G., Homayounfar, N., Wang, S., Lakshmikanth, S.K., Urtasun, R.: · 2018
Cited alongside, same era.
Calibrating uncertainties in object localization task
Phan, B., Salay, R., Czarnecki, K., Abdelzad, V., Denouden, T., Vernekar, S.: · 2018
Cited alongside, same era.
The tusimple lane challange, http://benchmark.tusimple.ai/
:
Cited in the paper.
Levi, D., Gispan, L., Giladi, N., Fetaya, E.: · 2019
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3d-lanenet: end-to-end 3d multiple lane detection
Garnett, N., Cohen, R., Pe’er, T., Lahav, R., Levi, D.: · 2019
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End-to-end lane detection through differentiable least-squares fitting
Van Gansbeke, W., De Brabandere, B., Neven, D., Proesmans, M., Van Gool, L.: · 2019
Later among the works it cites.
Dagmapper: Learning to map by discovering lane topology
Homayounfar, N., Ma, W.C., Liang, J., Wu, X., Fan, J., Urtasun, R.: · 2019
Later among the works it cites.