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\bibitem{adas}
A.~Bar~Hillel, R.~Lerner, D.~Levi, and G.~Raz, ``Recent progress in road and
lane detection: a survey,'' \emph{Machine vision and applications}, vol.~25,
no.~3, pp. 727--745, 2014.
\bibitem{scnn}
X.~Pan, J.~Shi, P.~Luo, X.~Wang, and X.~Tang, ``Spatial as deep: Spatial cnn
for traffic scene understanding,'' in \emph{Proceedings of the AAAI
conference on artificial intelligence}, vol.~32, no.~1, 2018.
\bibitem{polylanenet}
L.~Tabelini, R.~Berriel, T.~M. Paixao, C.~Badue, A.~F. De~Souza, and
T.~Oliveira-Santos, ``Polylanenet: Lane estimation via deep polynomial
regression,'' in \emph{2020 25th International Conference on Pattern
Recognition (ICPR)}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2021, pp.
6150--6156.
\bibitem{cannyedge}
J.~Canny, ``A computational approach to edge detection,'' \emph{IEEE
Transactions on pattern analysis and machine intelligence}, no.~6, pp.
679--698, 1986.
\bibitem{houghtransform}
J.~Illingworth and J.~Kittler, ``A survey of the hough transform,''
\emph{Computer vision, graphics, and image processing}, vol.~44, no.~1, pp.
87--116, 1988.
\bibitem{kluge1995deformable}
K.~Kluge and S.~Lakshmanan, ``A deformable-template approach to lane
detection,'' in \emph{Proceedings of the Intelligent Vehicles' 95.
Symposium}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 1995, pp. 54--59.
\bibitem{lstr}
R.~Liu, Z.~Yuan, T.~Liu, and Z.~Xiong, ``End-to-end lane shape prediction with
transformers,'' in \emph{Proceedings of the IEEE/CVF winter conference on
applications of computer vision}, 2021, pp. 3694--3702.
\bibitem{lanenet}
Z.~Wang, W.~Ren, and Q.~Qiu, ``Lanenet: Real-time lane detection networks for
autonomous driving,'' \emph{arXiv preprint arXiv:1807.01726}, 2018.
\bibitem{bezierlanenet}
Z.~Feng, S.~Guo, X.~Tan, K.~Xu, M.~Wang, and L.~Ma, ``Rethinking efficient lane
detection via curve modeling,'' in \emph{Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition}, 2022, pp.
17\,062--17\,070.
\bibitem{yolov10}
A.~Wang, H.~Chen, L.~Liu, K.~Chen, Z.~Lin, J.~Han, and G.~Ding, ``Yolov10:
Real-time end-to-end object detection,'' \emph{arXiv preprint
arXiv:2405.14458}, 2024.
\bibitem{fasterrcnn}
S.~Ren, K.~He, R.~Girshick, and J.~Sun, ``Faster r-cnn: Towards real-time
object detection with region proposal networks,'' \emph{IEEE transactions on
pattern analysis and machine intelligence}, vol.~39, no.~6, pp. 1137--1149,
2016.
\bibitem{laneatt}
L.~Tabelini, R.~Berriel, T.~M. Paixao, C.~Badue, A.~F. De~Souza, and
T.~Oliveira-Santos, ``Keep your eyes on the lane: Real-time attention-guided
lane detection,'' in \emph{Proceedings of the IEEE/CVF conference on computer
vision and pattern recognition}, 2021, pp. 294--302.
\bibitem{clrnet}
T.~Zheng, Y.~Huang, Y.~Liu, W.~Tang, Z.~Yang, D.~Cai, and X.~He, ``Clrnet:
Cross layer refinement network for lane detection,'' in \emph{Proceedings of
the IEEE/CVF conference on computer vision and pattern recognition}, 2022,
pp. 898--907.
\bibitem{nms}
A.~Neubeck and L.~Van~Gool, ``Efficient non-maximum suppression,'' in
\emph{18th international conference on pattern recognition (ICPR'06)},
vol.~3.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2006, pp. 850--855.
\bibitem{adnet}
L.~Xiao, X.~Li, S.~Yang, and W.~Yang, ``Adnet: Lane shape prediction via anchor
decomposition,'' in \emph{Proceedings of the IEEE/CVF International
Conference on Computer Vision}, 2023, pp. 6404--6413.
\bibitem{srlane}
C.~Chen, J.~Liu, C.~Zhou, J.~Tang, and G.~Wu, ``Sketch and refine: Towards fast
and accurate lane detection,'' in \emph{Proceedings of the AAAI Conference on
Artificial Intelligence}, vol.~38, no.~2, 2024, pp. 1001--1009.
\bibitem{clrernet}
H.~Honda and Y.~Uchida, ``Clrernet: improving confidence of lane detection with
laneiou,'' in \emph{Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision}, 2024, pp. 1176--1185.
\bibitem{gnn}
Z.~Wu, S.~Pan, F.~Chen, G.~Long, C.~Zhang, and S.~Y. Philip, ``A comprehensive
survey on graph neural networks,'' \emph{IEEE transactions on neural networks
and learning systems}, vol.~32, no.~1, pp. 4--24, 2020.
\bibitem{tusimple}
\BIBentryALTinterwordspacing
{TuSimple}, ``Tusimple benchmark,'' 2020, accessed: September 2020. [Online].
Available: \url{https://github.com/TuSimple/tusimple-benchmark/}
\BIBentrySTDinterwordspacing
\bibitem{llamas}
K.~Behrendt and R.~Soussan, ``Unsupervised labeled lane markers using maps,''
in \emph{Proceedings of the IEEE/CVF international conference on computer
vision workshops}, 2019, pp. 0--0.
\bibitem{curvelanes}
H.~Xu, S.~Wang, X.~Cai, W.~Zhang, X.~Liang, and Z.~Li, ``Curvelane-nas:
Unifying lane-sensitive architecture search and adaptive point blending,'' in
\emph{Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK,
August 23--28, 2020, Proceedings, Part XV 16}.\hskip 1em plus 0.5em minus
0.4em\relax Springer, 2020, pp. 689--704.
\bibitem{dalnet}
Z.~Yu, Q.~Liu, W.~Wang, L.~Zhang, and X.~Zhao, ``Dalnet: A rail detection
network based on dynamic anchor line,'' \emph{IEEE Transactions on
Instrumentation and Measurement}, 2024.
\bibitem{ufld}
Z.~Qin, H.~Wang, and X.~Li, ``Ultra fast structure-aware deep lane detection,''
in \emph{Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK,
August 23--28, 2020, Proceedings, Part XXIV 16}.\hskip 1em plus 0.5em minus
0.4em\relax Springer, 2020, pp. 276--291.
\bibitem{ufldv2}
Z.~Qin, P.~Zhang, and X.~Li, ``Ultra fast deep lane detection with hybrid
anchor driven ordinal classification,'' \emph{IEEE transactions on pattern
analysis and machine intelligence}, vol.~46, no.~5, pp. 2555--2568, 2022.
\bibitem{CondLaneNet}
L.~Liu, X.~Chen, S.~Zhu, and P.~Tan, ``Condlanenet: a top-to-down lane
detection framework based on conditional convolution,'' in \emph{Proceedings
of the IEEE/CVF international conference on computer vision}, 2021, pp.
3773--3782.
\bibitem{fololane}
Z.~Qin, P.~Zhang, and X.~Li, ``Ultra fast deep lane detection with hybrid
anchor driven ordinal classification,'' \emph{IEEE transactions on pattern
analysis and machine intelligence}, vol.~46, no.~5, pp. 2555--2568, 2022.
\bibitem{ganet}
M.~Morley, R.~Atkinson, D.~Savi{\'c}, and G.~Walters, ``Ganet: genetic
algorithm platform for pipe network optimisation,'' \emph{Advances in
engineering software}, vol.~32, no.~6, pp. 467--475, 2001.
\bibitem{bsnet}
H.~Chen, M.~Wang, and Y.~Liu, ``Bsnet: Lane detection via draw b-spline curves
nearby,'' \emph{arXiv preprint arXiv:2301.06910}, 2023.
\bibitem{yolox}
G.~Zheng, L.~Songtao, W.~Feng, L.~Zeming, S.~Jian \emph{et~al.}, ``Yolox:
Exceeding yolo series in 2021,'' \emph{arXiv preprint arXiv:2107.08430},
2021.
\bibitem{sparse}
J.~Liu, Z.~Zhang, M.~Lu, H.~Wei, D.~Li, Y.~Xie, J.~Peng, L.~Tian, A.~Sirasao,
and E.~Barsoum, ``Sparse laneformer,'' \emph{arXiv preprint
arXiv:2404.07821}, 2024.
\bibitem{detr}
N.~Carion, F.~Massa, G.~Synnaeve, N.~Usunier, A.~Kirillov, and S.~Zagoruyko,
``End-to-end object detection with transformers,'' in \emph{European
conference on computer vision}.\hskip 1em plus 0.5em minus 0.4em\relax
Springer, 2020, pp. 213--229.
\bibitem{o2o}
P.~Sun, Y.~Jiang, E.~Xie, W.~Shao, Z.~Yuan, C.~Wang, and P.~Luo, ``What makes
for end-to-end object detection?'' in \emph{International Conference on
Machine Learning}.\hskip 1em plus 0.5em minus 0.4em\relax PMLR, 2021, pp.
9934--9944.
\bibitem{learnNMS}
J.~Hosang, R.~Benenson, and B.~Schiele, ``Learning non-maximum suppression,''
in \emph{Proceedings of the IEEE conference on computer vision and pattern
recognition}, 2017, pp. 4507--4515.
\bibitem{date}
Y.~Chen, Q.~Chen, Q.~Hu, and J.~Cheng, ``Date: Dual assignment for end-to-end
fully convolutional object detection,'' \emph{arXiv preprint
arXiv:2211.13859}, 2022.
\bibitem{o3d}
J.~Wang, L.~Song, Z.~Li, H.~Sun, J.~Sun, and N.~Zheng, ``End-to-end object
detection with fully convolutional network,'' in \emph{Proceedings of the
IEEE/CVF conference on computer vision and pattern recognition}, 2021, pp.
15\,849--15\,858.
\bibitem{relationnet}
H.~Hu, J.~Gu, Z.~Zhang, J.~Dai, and Y.~Wei, ``Relation networks for object
detection,'' in \emph{Proceedings of the IEEE conference on computer vision
and pattern recognition}, 2018, pp. 3588--3597.
\bibitem{resnet}
K.~He, X.~Zhang, S.~Ren, and J.~Sun, ``Deep residual learning for image
recognition,'' in \emph{Proceedings of the IEEE conference on computer vision
and pattern recognition}, 2016, pp. 770--778.
\bibitem{fpn}
T.-Y. Lin, P.~Doll{\'a}r, R.~Girshick, K.~He, B.~Hariharan, and S.~Belongie,
``Feature pyramid networks for object detection,'' in \emph{Proceedings of
the IEEE conference on computer vision and pattern recognition}, 2017, pp.
2117--2125.
\bibitem{linecnn}
X.~Li, J.~Li, X.~Hu, and J.~Yang, ``Line-cnn: End-to-end traffic line detection
with line proposal unit,'' \emph{IEEE Transactions on Intelligent
Transportation Systems}, vol.~21, no.~1, pp. 248--258, 2019.
\bibitem{vanishing}
Y.~Su, Y.~Zhang, T.~Lu, J.~Yang, and H.~Kong, ``Vanishing point constrained
lane detection with a stereo camera,'' \emph{IEEE Transactions on Intelligent
Transportation Systems}, vol.~19, no.~8, pp. 2739--2744, 2017.
\bibitem{dualassign}
S.~Li, C.~He, R.~Li, and L.~Zhang, ``A dual weighting label assignment scheme
for object detection,'' in \emph{Proceedings of the IEEE/CVF conference on
computer vision and pattern recognition}, 2022, pp. 9387--9396.
\bibitem{yolact}
D.~Bolya, C.~Zhou, F.~Xiao, and Y.~J. Lee, ``Yolact: Real-time instance
segmentation,'' in \emph{Proceedings of the IEEE/CVF international conference
on computer vision}, 2019, pp. 9157--9166.
\bibitem{pointnet}
C.~R. Qi, H.~Su, K.~Mo, and L.~J. Guibas, ``Pointnet: Deep learning on point
sets for 3d classification and segmentation,'' in \emph{Proceedings of the
IEEE conference on computer vision and pattern recognition}, 2017, pp.
652--660.
\bibitem{focal}
T.-Y. Lin, P.~Goyal, R.~Girshick, K.~He, and P.~Doll{\'a}r, ``Focal loss for
dense object detection,'' in \emph{Proceedings of the IEEE international
conference on computer vision}, 2017, pp. 2980--2988.
\bibitem{pss}
Q.~Zhou and C.~Yu, ``Object detection made simpler by eliminating heuristic
nms,'' \emph{IEEE Transactions on Multimedia}, vol.~25, pp. 9254--9262, 2023.
\bibitem{adam}
D.~P. Kingma, ``Adam: A method for stochastic optimization,'' \emph{arXiv
preprint arXiv:1412.6980}, 2014.
\bibitem{dla}
F.~Yu, D.~Wang, E.~Shelhamer, and T.~Darrell, ``Deep layer aggregation,'' in
\emph{Proceedings of the IEEE conference on computer vision and pattern
recognition}, 2018, pp. 2403--2412.
\bibitem{resa}
T.~Zheng, H.~Fang, Y.~Zhang, W.~Tang, Z.~Yang, H.~Liu, and D.~Cai, ``Resa:
Recurrent feature-shift aggregator for lane detection,'' in \emph{Proceedings
of the AAAI conference on artificial intelligence}, vol.~35, no.~4, 2021, pp.
3547--3554.
\bibitem{laneaf}
H.~Abualsaud, S.~Liu, D.~B. Lu, K.~Situ, A.~Rangesh, and M.~M. Trivedi,
``Laneaf: Robust multi-lane detection with affinity fields,'' \emph{IEEE
Robotics and Automation Letters}, vol.~6, no.~4, pp. 7477--7484, 2021.
\bibitem{eigenlanes}
D.~Jin, W.~Park, S.-G. Jeong, H.~Kwon, and C.-S. Kim, ``Eigenlanes: Data-driven
lane descriptors for structurally diverse lanes,'' in \emph{Proceedings of
the IEEE/CVF conference on computer vision and pattern recognition}, 2022,
pp. 17\,163--17\,171.
\bibitem{enetsad}
Y.~Hou, Z.~Ma, C.~Liu, and C.~C. Loy, ``Learning lightweight lane detection
cnns by self attention distillation,'' in \emph{Proceedings of the IEEE/CVF
international conference on computer vision}, 2019, pp. 1013--1021.
\bibitem{pointlanenet}
Z.~Chen, Q.~Liu, and C.~Lian, ``Pointlanenet: Efficient end-to-end cnns for
accurate real-time lane detection,'' in \emph{2019 IEEE intelligent vehicles
symposium (IV)}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2019, pp.
2563--2568.
\bibitem{iouloss}
J.~Yu, Y.~Jiang, Z.~Wang, Z.~Cao, and T.~Huang, ``Unitbox: An advanced object
detection network,'' in \emph{Proceedings of the 24th ACM international
conference on Multimedia}, 2016, pp. 516--520.
\bibitem{giouloss}
H.~Rezatofighi, N.~Tsoi, J.~Gwak, A.~Sadeghian, I.~Reid, and S.~Savarese,
``Generalized intersection over union: A metric and a loss for bounding box
regression,'' in \emph{Proceedings of the IEEE/CVF conference on computer
vision and pattern recognition}, 2019, pp. 658--666.
\end{thebibliography}

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\bibitem{detr}
N.~Carion, F.~Massa, G.~Synnaeve, N.~Usunier, A.~Kirillov, and S.~Zagoruyko,
``End-to-end object detection with transformers,'' in \emph{European
conference on computer vision}.\hskip 1em plus 0.5em minus 0.4em\relax
Springer, 2020, pp. 213--229.
\bibitem{learnNMS}
J.~Hosang, R.~Benenson, and B.~Schiele, ``Learning non-maximum suppression,''
in \emph{Proceedings of the IEEE conference on computer vision and pattern
recognition}, 2017, pp. 4507--4515.
\bibitem{yolov10}
A.~Wang, H.~Chen, L.~Liu, K.~Chen, Z.~Lin, J.~Han, and G.~Ding, ``Yolov10:
Real-time end-to-end object detection,'' \emph{arXiv preprint
arXiv:2405.14458}, 2024.
\bibitem{o2o}
P.~Sun, Y.~Jiang, E.~Xie, W.~Shao, Z.~Yuan, C.~Wang, and P.~Luo, ``What makes
for end-to-end object detection?'' in \emph{International Conference on
Machine Learning}.\hskip 1em plus 0.5em minus 0.4em\relax PMLR, 2021, pp.
9934--9944.
\bibitem{o3d}
J.~Wang, L.~Song, Z.~Li, H.~Sun, J.~Sun, and N.~Zheng, ``End-to-end object
detection with fully convolutional network,'' in \emph{Proceedings of the
IEEE/CVF conference on computer vision and pattern recognition}, 2021, pp.
15\,849--15\,858.
\bibitem{relationnet}
H.~Hu, J.~Gu, Z.~Zhang, J.~Dai, and Y.~Wei, ``Relation networks for object
detection,'' in \emph{Proceedings of the IEEE conference on computer vision
and pattern recognition}, 2018, pp. 3588--3597.
\bibitem{yolact}
D.~Bolya, C.~Zhou, F.~Xiao, and Y.~J. Lee, ``Yolact: Real-time instance
segmentation,'' in \emph{Proceedings of the IEEE/CVF international conference
on computer vision}, 2019, pp. 9157--9166.
\bibitem{iouloss}
J.~Yu, Y.~Jiang, Z.~Wang, Z.~Cao, and T.~Huang, ``Unitbox: An advanced object
detection network,'' in \emph{Proceedings of the 24th ACM international
conference on Multimedia}, 2016, pp. 516--520.
\bibitem{giouloss}
H.~Rezatofighi, N.~Tsoi, J.~Gwak, A.~Sadeghian, I.~Reid, and S.~Savarese,
``Generalized intersection over union: A metric and a loss for bounding box
regression,'' in \emph{Proceedings of the IEEE/CVF conference on computer
vision and pattern recognition}, 2019, pp. 658--666.
\bibitem{clrnet}
T.~Zheng, Y.~Huang, Y.~Liu, W.~Tang, Z.~Yang, D.~Cai, and X.~He, ``Clrnet:
Cross layer refinement network for lane detection,'' in \emph{Proceedings of
the IEEE/CVF conference on computer vision and pattern recognition}, 2022,
pp. 898--907.
\bibitem{adnet}
L.~Xiao, X.~Li, S.~Yang, and W.~Yang, ``Adnet: Lane shape prediction via anchor
decomposition,'' in \emph{Proceedings of the IEEE/CVF International
Conference on Computer Vision}, 2023, pp. 6404--6413.
\bibitem{date}
Y.~Chen, Q.~Chen, Q.~Hu, and J.~Cheng, ``Date: Dual assignment for end-to-end
fully convolutional object detection,'' \emph{arXiv preprint
arXiv:2211.13859}, 2022.
\bibitem{clrernet}
H.~Honda and Y.~Uchida, ``Clrernet: improving confidence of lane detection with
laneiou,'' in \emph{Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision}, 2024, pp. 1176--1185.
\bibitem{yolox}
G.~Zheng, L.~Songtao, W.~Feng, L.~Zeming, S.~Jian \emph{et~al.}, ``Yolox:
Exceeding yolo series in 2021,'' \emph{arXiv preprint arXiv:2107.08430},
2021.
\end{thebibliography}

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