Fetching the paper…
Reading the bibliography…
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
K. Kluge and S. Lakshmanan, “A deformable-template approach to lane detection,” in Proceedings of the Intelligent Vehicles Symposium . IEEE, 1995, pp. 54–59
1995
Earlier work this paper cites.
K.-Y. Chiu and S.-F. Lin, “Lane Detection using Color-based Segmentation,” in Proceedings Intelligent Vehicles Symposium . IEEE, 2005, pp. 706–711
2005
Earlier work this paper cites.
C. R. Jung and C. R. Kelber, “Lane Following and Lane Departure Using a Linear-Parabolic Model,” Image and Vision Computing , vol. 23, no. 13, pp. 1192–1202, 2005
2005
Earlier work this paper cites.
J. C. McCall and M. M. Trivedi, “Video Based Lane Estimation and Tracking for. Driver Assistance: Survey, System, and Evaluation,” IEEE Transactions on Intelligent Transportation Systems , vol. 7, no. 1, pp. 20–37, 2006
2006
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009, pp. 248–255
2009
Earlier work this paper cites.
R. K. Satzoda and M. M. Trivedi, “On Performance Evaluation Metrics for Lane Estimation,” in International Conference on Pattern Recognition (ICPR) . IEEE, 2014, pp. 2625–2630
2014
Earlier work this paper cites.
R. F. Berriel, E. de Aguiar, V. V. de Souza Filho, and T. Oliveira-Santos, “A Particle Filter-based Lane Marker Tracking Approach Using a Cubic Spline Model,” in 28th SIBGRAPI Conference on Graphics, Patterns and Images . IEEE, 2015, pp. 149–156
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. Gurghian, T. Koduri, S. V. Bailur, K. J. Carey, and V. N. Murali, “DeepLanes: End-To-End Lane Position Estimation using Deep Neural Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , 2016, pp. 38–45
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Cited alongside, same era.
R. F. Berriel, E. de Aguiar, A. F. De Souza, and T. Oliveira-Santos, “Ego-Lane Analysis System (ELAS): Dataset and Algorithms,” Image and Vision Computing , vol. 68, pp. 64–75, 2017
2017
Cited alongside, same era.
X. Pan, J. Shi, P. Luo, X. Wang, and X. Tang, “Spatial As Deep: Spatial CNN for Traffic Scene Understanding,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Cited alongside, same era.
Y. Hou, Z. Ma, C. Liu, and C. C. Loy, “Learning Lightweight Lane Detection CNNs by Self Attention Distillation,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 1013–1021
2019
Later among the works it cites.
J. Philion, “FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 11 582–11 591
2019
Later among the works it cites.
2019
Later among the works it cites.
K. Behrendt and R. Soussan, “Unsupervised labeled lane marker dataset generation using maps,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
L. C. Possatti, R. Guidolini, V. B. Cardoso, R. F. Berriel, T. M. Paixão, C. Badue, A. F. De Souza, and T. Oliveira-Santos, “Traffic light recognition using deep learning and prior maps for autonomous cars,” in 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2019, pp. 1–8
2019
Cited alongside, same era.
X. Li, J. Li, X. Hu, and J. Yang, “Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 1, pp. 248–258, 2019
2019
Cited alongside, same era.
TuSimple. TuSimple Benchmark. [Online]. Available: https://github.com/TuSimple/tusimple-benchmark
Cited in the paper.
M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning (ICML) , 2019, pp. 6105–6114
2019
Later among the works it cites.
P. Yang, G. Zhang, L. Wang, L. Xu, Q. Deng, and M.-H. Yang, “A part-aware multi-scale fully convolutional network for pedestrian detection,” IEEE Transactions on Intelligent Transportation Systems , 2020
2020
Closest in time.
D. Feng, C. Haase-Schütz, L. Rosenbaum, H. Hertlein, C. Glaeser, F. Timm, W. Wiesbeck, and K. Dietmayer, “Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges,” IEEE Transactions on Intelligent Transportation Systems , 2020
2020
Closest in time.