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Earlier: A2, A1, A3:
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1311
Lane model
BibRef
Mathibela, B.,
Newman, P.,
Posner, I.,
Reading the Road: Road Marking Classification and Interpretation,
ITS(16), No. 4, August 2015, pp. 2072-2081.
IEEE DOI
1508
Feature extraction
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Jung, C.R.,
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Image edge detection
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Cortés, A.[Andoni],
Otaegui, O.[Oihana],
Arróspide, J.[Jon],
Salgado, L.[Luis],
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Xue, J.,
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1604
Accuracy
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Du, X.,
Tan, K.K.,
Comprehensive and Practical Vision System for Self-Driving Vehicle
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IP(25), No. 5, May 2016, pp. 2075-2088.
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1604
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Lu, J.[Jie],
Xu, M.L.[Ming-Liang],
Lv, P.[Pei],
Zhao, X.[Xiaoke],
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Lane detection
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Srinivasa Murthy, S.,
Suddamalla, U.,
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modified min-between-max thresholding
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Xu, S.,
Wang, H.,
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Pan, C.,
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Automobiles, Data mining, Feature extraction, Image segmentation,
Neural networks, Remote sensing, Roads,
Cascaded convolutional neural network (CasNet), end-to-end,
road centerline extraction, road, detection
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Piao, J.C.[Jing-Chun],
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Yoo, J.H.,
Lee, S.W.,
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Kim, D.H.,
A Robust Lane Detection Method Based on Vanishing Point Estimation
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ITS(18), No. 12, December 2017, pp. 3254-3266.
IEEE DOI
1712
Estimation, Feature extraction, Image color analysis,
Image segmentation, Probabilistic logic, Roads, Robustness,
vanishing point estimation
BibRef
Vivacqua, R.P.D.,
Bertozzi, M.,
Cerri, P.,
Martins, F.N.,
Vassallo, R.F.,
Self-Localization Based on Visual Lane Marking Maps: An Accurate
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ITS(19), No. 2, February 2018, pp. 582-597.
IEEE DOI
1802
Cameras, Laser radar, Roads, Robustness, Sensors, Visualization,
Autonomous driving, dead reckoning,
mapping and localization
BibRef
Ye, Y.Y.[Yang Yang],
Hao, X.L.[Xiao Li],
Chen, H.J.[Hou Jin],
Lane detection method based on lane structural analysis and CNNs,
IET-ITS(12), No. 6, August 2018, pp. 513-520.
DOI Link
1807
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John, V.[Vijay],
Liu, Z.[Zheng],
Mita, S.[Seiichi],
Guo, C.Z.[Chun-Zhao],
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1808
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Earlier: A1, A2, A4, A3, A5:
Real-Time Lane Estimation Using Deep Features and Extra Trees
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PSIVT15(721-733).
Springer DOI
1602
BibRef
John, V.[Vijay],
Karunakaran, N.M.,
Guo, C.Z.[Chun-Zhao],
Kidono, K.[Kiyosumi],
Mita, S.[Seiichi],
Free Space, Visible and Missing Lane Marker Estimation using the
PsiNet and Extra Trees Regression,
ICPR18(189-194)
IEEE DOI
1812
Roads, Feature extraction, Estimation, Regression tree analysis,
Semantics, Image segmentation
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Park, J.M.[Jeong Min],
Lee, J.W.[Joon Woong],
Lane estimation by particle-filtering combined with likelihood
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Elsevier DOI
1811
Probabilistic lane estimation, Likelihood computation,
Motion compensation, ROI weighting, Two-step particle filtering
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Lee, C.,
Moon, J.,
Robust Lane Detection and Tracking for Real-Time Applications,
ITS(19), No. 12, December 2018, pp. 4043-4048.
IEEE DOI
1812
Image color analysis, Image segmentation, Image edge detection,
Roads, Gray-scale, Real-time systems, Robustness, Lane detection,
Kalman filter
BibRef
Martín-Jiménez, J.A.[José Antonio],
Zazo, S.[Santiago],
Justel, J.J.A.[José Juan Arranz],
Rodríguez-Gonzálvez, P.[Pablo],
González-Aguilera, D.[Diego],
Road safety evaluation through automatic extraction of road
horizontal alignments from Mobile LiDAR System and inductive
reasoning based on a decision tree,
PandRS(146), 2018, pp. 334-346.
Elsevier DOI
1812
Road safety, Decision tree, Geometric design consistency,
Horizontal alignment parameters, Mobile LiDAR System
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Point cloud, Road marking, Extraction, Classification,
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2206
Roads, Satellites, Data mining, Feature extraction, Sensors, Geometry,
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Feature extraction, Computer architecture, Histograms, Training,
Microprocessors, Prototypes, Dual feature, HOG, Haar-like,
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Ustunel, E.[Eser],
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Vision-based road slope estimation methods using road lines or local
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2001
Proposals, Task analysis, Detectors, Feature extraction,
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Lane detection, Image quality, Convolution neural network
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2005
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WACV20(1823-1832)
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2006
Roads, Image segmentation, Feature extraction, Machine learning,
Prediction algorithms, Parallel processing, Detectors
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Hu, C.,
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2007
Rollover, Stability analysis, Transient analysis, Tires, Safety,
Adaptation models, Autonomous vehicles, lane keeping,
rollover prevention
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2008
Cycling lane, Centerline, Mobile LiDAR, Roadside barrier,
Object identification, Methodology
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Ye, X.Y.[Xing-Yu],
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Deep learning, Road marking, Spatial transform,
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2011
Task analysis, Decoding, Object detection, Semantics,
Intelligent vehicles, Roads, Neural networks, Multi-task network,
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Lane Width Estimation in Work Zones Using LiDAR-Based Mobile Mapping
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2012
Roads, Laser radar, Feature extraction,
Data mining, Surface morphology, Accidents, Lane width estimation,
wide lanes
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Wei, Y.,
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Simultaneous Road Surface and Centerline Extraction From Large-Scale
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IEEE DOI
2012
Roads, Image segmentation, Remote sensing, Boosting,
Feature extraction, Surface topography, Semantics,
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A review of lane detection methods based on deep learning,
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2012
Lane detection, Deep learning, Semantic segmentation, Instance segmentation
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Lu, P.,
Xu, S.,
Peng, H.,
Graph-Embedded Lane Detection,
IP(30), 2021, pp. 2977-2988.
IEEE DOI
2102
Feature extraction, Lane detection, Topology, Fitting, Roads, Geometry,
Semantics, Lane detection, graph representation,
deep learning
BibRef
Zhang, Y.,
Lu, Z.,
Ma, D.,
Xue, J.H.,
Liao, Q.,
Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection
Network and Wasserstein GAN,
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2103
Roads, Feature extraction, Semantics, Interference,
Training, Image segmentation, Lane line detection,
Ripple-GAN
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Zhu, D.[Di],
Song, R.[Rui],
Chen, H.[Hui],
Klette, R.[Reinhard],
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SP:IC(95), 2021, pp. 116230.
Elsevier DOI
2106
ADAS, Multi-lane detection, Multi-lane tracking, Moments, Kalman filter
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Yao, L.[Lianbi],
Qin, C.C.[Chang-Cai],
Chen, Q.C.[Qi-Chao],
Wu, H.B.[Hang-Bin],
Automatic Road Marking Extraction and Vectorization from
Vehicle-Borne Laser Scanning Data,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Xu, X.M.[Xue-Miao],
Yu, T.F.[Tian-Fei],
Hu, X.W.[Xiao-Wei],
Ng, W.W.Y.[Wing W. Y.],
Heng, P.A.[Pheng-Ann],
SALMNet: A Structure-Aware Lane Marking Detection Network,
ITS(22), No. 8, August 2021, pp. 4986-4997.
IEEE DOI
2108
Feature extraction, Roads, Convolution, Semantics, Benchmark testing,
Computer science, Convolutional neural networks,
intelligent transportation system
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Wen, T.[Tuopu],
Yang, D.[Diange],
Jiang, K.[Kun],
Yu, C.L.[Chun-Lei],
Lin, J.X.[Jia-Xin],
Wijaya, B.[Benny],
Jiao, X.Y.[Xin-Yu],
Bridging the Gap of Lane Detection Performance Between Different
Datasets: Unified Viewpoint Transformation,
ITS(22), No. 10, October 2021, pp. 6198-6207.
IEEE DOI
2110
Feature extraction, Robustness, Semantics, Adaptation models,
Task analysis, Deep learning, Lane detection,
advanced driver assistant systems (ADAS)
BibRef
Cheng, S.[Shuo],
Li, L.[Liang],
Liu, Y.G.[Yong-Gang],
Li, W.B.[Wei-Bing],
Guo, H.Q.[Hong-Qiang],
Virtual Fluid-Flow-Model-Based Lane-Keeping Integrated With Collision
Avoidance Control System Design for Autonomous Vehicles,
ITS(22), No. 10, October 2021, pp. 6232-6241.
IEEE DOI
2110
Roads, Collision avoidance, Vehicles, Mathematical model, Stress,
Vehicle dynamics, Virtual fluid-flow-model, lane-keeping,
path planning and tracking
BibRef
Haris, M.[Malik],
Hou, J.[Jin],
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Multi-scale spatial convolution algorithm for lane line detection and
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Elsevier DOI
2111
Unmanned vehicle, Lane line detection, Lane offset estimation,
Convolutional neural network (CNN), Scale perception, Multi-tasking
BibRef
Luo, S.[Sheng],
Zhang, X.Q.[Xiao-Qin],
Hu, J.[Jie],
Xu, J.H.[Jing-Hua],
Multiple Lane Detection via Combining Complementary Structural
Constraints,
ITS(22), No. 12, December 2021, pp. 7597-7606.
IEEE DOI
2112
Roads, Image edge detection, Transforms, Robustness, Lighting,
Feature extraction, Lane detection, Hough transform, dynamic programming
BibRef
Kang, C.M.[Chang Mook],
Kim, W.[Wonhee],
Linear Parameter Varying Observer for Lane Estimation Using Cylinder
Domain in Vehicles,
ITS(22), No. 11, November 2021, pp. 7030-7039.
IEEE DOI
2112
Observers, Roads, Cameras, Vision sensors, Tires, Autonomous vehicles,
Vehicle, lane change, state observer, cylinder domain
BibRef
Xiao, D.[Degui],
Zhuo, L.[Lin],
Li, J.[Jianfang],
Li, J.Z.[Jia-Zhi],
Structure-prior deep neural network for lane detection,
JVCIR(81), 2021, pp. 103373.
Elsevier DOI
2112
Lane marking detection, Deep neural network, Structure-prior
BibRef
Yin, R.[Ruochen],
Cheng, Y.[Yong],
Wu, H.[Huapeng],
Song, Y.T.[Yun-Tao],
Yu, B.[Biao],
Niu, R.[Runxin],
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic
Segmentation Using Deep Neural Networks,
ITS(23), No. 2, February 2022, pp. 1543-1553.
IEEE DOI
2202
Semantics, Image segmentation, Cameras, Laser radar, Neural networks,
Meters, Lane marking, semantic segmentation, LIDAR-camera fusion,
LSTM
BibRef
Trogh, J.[Jens],
Botteldooren, D.[Dick],
de Coensel, B.[Bert],
Martens, L.[Luc],
Joseph, W.[Wout],
Plets, D.[David],
Map Matching and Lane Detection Based on Markovian Behavior, GIS, and
IMU Data,
ITS(23), No. 3, March 2022, pp. 2056-2070.
IEEE DOI
2203
Global Positioning System, Hidden Markov models, Trajectory,
Automobiles, Noise measurement, Map matching, lane detection, GPS,
data fusion
BibRef
Chen, S.[Sihan],
Huang, L.[Libo],
Chen, H.[Huanlei],
Bai, J.[Jie],
Multi-Lane Detection and Tracking Using Temporal-Spatial Model and
Particle Filtering,
ITS(23), No. 3, March 2022, pp. 2227-2245.
IEEE DOI
2203
Lane detection, Feature extraction, Geometry,
Computational modeling, Robustness, Roads, Radar tracking,
lane tracking
BibRef
Martirena, J.B.[Javier Barandiarán],
Doncel, M.N.[Marcos Nieto],
Vidal, A.C.[Andoni Cortés],
Madurga, O.O.[Oihana Otaegui],
Esnal, J.F.[Julián Flórez],
Romay, M.G.[Manuel Graña],
Automated Annotation of Lane Markings Using LIDAR and Odometry,
ITS(23), No. 4, April 2022, pp. 3115-3125.
IEEE DOI
2204
Annotations, Laser radar, Roads, Image segmentation, Manuals, Lasers,
Autonomous driving, lane sensing, lane detection, lane marking,
annotation
BibRef
Li, K.[Kan],
Yang, X.Y.[Xiao-Yu],
Luo, Y.H.[Yue-Hui],
Li, H.Y.[Hui-Yun],
Road geometry perception without accurate positioning and lane
information,
IET-ITS(16), No. 7, 2022, pp. 940-957.
DOI Link
2206
BibRef
Zhang, Y.C.[You-Cheng],
Lu, Z.Q.[Zong-Qing],
Zhang, X.C.[Xue-Chen],
Xue, J.H.[Jing-Hao],
Liao, Q.M.[Qing-Min],
Deep Learning in Lane Marking Detection: A Survey,
ITS(23), No. 7, July 2022, pp. 5976-5992.
IEEE DOI
2207
Deep learning, Feature extraction, Roads, Lighting, Semantics,
Optimization, Videos, Lane marking detection, traffic dataset,
evaluation metric
BibRef
Zhang, J.Y.[Ji-Yong],
Deng, T.[Tao],
Yan, F.[Fei],
Liu, W.[Wenbo],
Lane Detection Model Based on Spatio-Temporal Network With Double
Convolutional Gated Recurrent Units,
ITS(23), No. 7, July 2022, pp. 6666-6678.
IEEE DOI
2207
Lane detection, Feature extraction, Roads, Image segmentation,
Semantics, Logic gates, Deep learning, Lane detection, end-to-end,
convolutional neural network
BibRef
Shao, M.E.[Mei-En],
Haq, M.A.[Muhamad Amirul],
Gao, D.Q.[De-Qin],
Chondro, P.[Peter],
Ruan, S.J.[Shanq-Jang],
Semantic Segmentation for Free Space and Lane Based on Grid-Based
Interest Point Detection,
ITS(23), No. 7, July 2022, pp. 8498-8512.
IEEE DOI
2207
Task analysis, Image segmentation, Semantics, Neural networks,
Lane detection, Feature extraction, Object detection,
semantic segmentation
BibRef
Ko, Y.[Yeongmin],
Lee, Y.[Younkwan],
Azam, S.[Shoaib],
Munir, F.[Farzeen],
Jeon, M.[Moongu],
Pedrycz, W.[Witold],
Key Points Estimation and Point Instance Segmentation Approach for
Lane Detection,
ITS(23), No. 7, July 2022, pp. 8949-8958.
IEEE DOI
2207
Semantics, Feature extraction, Estimation, Training, Lane detection,
Image segmentation, Deep learning, Lane detection, deep learning
BibRef
Munir, F.[Farzeen],
Azam, S.[Shoaib],
Jeon, M.[Moongu],
Lee, B.G.[Byung-Geun],
Pedrycz, W.[Witold],
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic
Vision Sensor,
ITS(23), No. 7, July 2022, pp. 9318-9334.
IEEE DOI
2207
Cameras, Task analysis, Autonomous vehicles, Lane detection,
Decoding, Brightness, Feature extraction, Lane marking detection,
attention network
BibRef
Ren, F.L.[Feng-Lei],
Zhou, H.B.[Hai-Bo],
Yang, L.[Lu],
Liu, F.[Fulong],
He, X.[Xin],
ADPNet: Attention based dual path network for lane detection,
JVCIR(87), 2022, pp. 103574.
Elsevier DOI
2208
Lane detection, Semantic segmentation, Attention mechanism, Lane fitting
BibRef
Pang, G.[Guilin],
Zhang, B.[Baopeng],
Teng, Z.[Zhu],
Ma, N.[Nan],
Fan, J.P.[Jian-Ping],
Fast-HBNet: Hybrid Branch Network for Fast Lane Detection,
ITS(23), No. 9, September 2022, pp. 15673-15683.
IEEE DOI
2209
Lane detection, Feature extraction, Semantics, Real-time systems,
Detectors, Representation learning, Roads, Lane detection,
hierarchical feature learning
BibRef
Zhou, Y.[Yujing],
Wang, Z.[Zejiang],
Wang, J.[Junmin],
Illumination-Resilient Lane Detection by Threshold Self-Adjustment
Using Newton-Based Extremum Seeking,
ITS(23), No. 10, October 2022, pp. 18643-18654.
IEEE DOI
2210
Image color analysis, Lane detection, Lighting, Cost function,
Image edge detection, Estimation, Feature extraction,
extremum seeking
BibRef
Yao, Z.Y.[Zi-Ying],
Wu, X.K.[Xin-Kai],
Wang, P.C.[Peng-Cheng],
Ding, C.[Chuan],
DevNet: Deviation Aware Network for Lane Detection,
ITS(23), No. 10, October 2022, pp. 17584-17593.
IEEE DOI
2210
Feature extraction, Lane detection, Estimation, Shape, Semantics,
Microprocessors, Autonomous vehicles, lane detection, driving assistance
BibRef
Yang, J.X.[Jia-Xing],
Zhang, L.[Lihe],
Lu, H.C.[Hu-Chuan],
Lane Detection with Versatile AtrousFormer and Local Semantic
Guidance,
PR(133), 2023, pp. 109053.
Elsevier DOI
2210
Lane detection, Global AtrousFormer, Local AtrousFormer,
Enhanced feature extractor, Local semantic guided decoder
BibRef
Katariya, V.[Vinit],
Baharani, M.[Mohammadreza],
Morris, N.[Nichole],
Shoghli, O.[Omidreza],
Tabkhi, H.[Hamed],
DeepTrack: Lightweight Deep Learning for Vehicle Trajectory
Prediction in Highways,
ITS(23), No. 10, October 2022, pp. 18927-18936.
IEEE DOI
2210
Trajectory, Predictive models, Computational modeling, Convolution,
Accidents, Deep learning, Real-time systems, DeepTrack
BibRef
Wang, Y.[Yuhao],
Wang, Y.H.[Yu-Hong],
Ho, I.W.H.[Ivan Wang-Hei],
Sheng, W.[Wei],
Chen, L.[Ling],
Pavement Marking Incorporated With Binary Code for Accurate
Localization of Autonomous Vehicles,
ITS(23), No. 11, November 2022, pp. 22290-22300.
IEEE DOI
2212
Roads, Location awareness, Autonomous vehicles,
Global Positioning System, Image color analysis, Cameras, binary code
BibRef
Iparraguirre, O.[Olatz],
Iturbe-Olleta, N.[Nagore],
Brazalez, A.[Alfonso],
Borro, D.[Diego],
Road Marking Damage Detection Based on Deep Learning for
Infrastructure Evaluation in Emerging Autonomous Driving,
ITS(23), No. 11, November 2022, pp. 22378-22385.
IEEE DOI
2212
Roads, Automation, Vehicles, Maintenance engineering, Inspection,
Detectors, Deep learning, Autonomous driving,
deep learning object detector
BibRef
Qiu, Z.[Zengyu],
Zhao, J.[Jing],
Sun, S.L.[Shi-Liang],
MFIALane: Multiscale Feature Information Aggregator Network for Lane
Detection,
ITS(23), No. 12, December 2022, pp. 24263-24275.
IEEE DOI
2212
Lane detection, Feature extraction, Semantics, Task analysis,
Decoding, Mathematical models, Proposals, Deep learning, autonomous driving
BibRef
Zheng, S.[Shaowu],
Xie, Y.[Yun],
Li, M.H.[Ming-Hao],
Xie, C.[Chong],
Li, W.H.[Wei-Hua],
A Novel Strategy for Global Lane Detection Based on Key-Point
Regression and Multi-Scale Feature Fusion,
ITS(23), No. 12, December 2022, pp. 23244-23253.
IEEE DOI
2212
Feature extraction, Lane detection, Computational modeling,
Image segmentation, Image edge detection,
deep learning
BibRef
Zhang, H.[Han],
Gu, Y.C.[Yun-Chao],
Wang, X.L.[Xin-Liang],
Pan, J.J.[Jun-Jun],
Wang, M.H.[Ming-Hui],
Lane Detection Transformer Based on Multi-frame Horizontal and Vertical
Attention and Visual Transformer Module,
ECCV22(XXIX:1-16).
Springer DOI
2211
BibRef
Chen, L.[Li],
Sima, C.H.[Chong-Hao],
Li, Y.[Yang],
Zheng, Z.[Zehan],
Xu, J.J.[Jia-Jie],
Geng, X.W.[Xiang-Wei],
Li, H.Y.[Hong-Yang],
He, C.H.[Cong-Hui],
Shi, J.P.[Jian-Ping],
Qiao, Y.[Yu],
Yan, J.C.[Jun-Chi],
PersFormer: 3D Lane Detection via Perspective Transformer and the
OpenLane Benchmark,
ECCV22(XXXVIII:550-567).
Springer DOI
2211
BibRef
Xu, S.H.[Sheng-Hua],
Cai, X.Y.[Xin-Yue],
Zhao, B.[Bin],
Zhang, L.[Li],
Xu, H.[Hang],
Fu, Y.W.[Yan-Wei],
Xue, X.Y.[Xiang-Yang],
RCLane: Relay Chain Prediction for Lane Detection,
ECCV22(XXXVIII:461-477).
Springer DOI
2211
BibRef
Yang, H.[Hao],
Lin, S.Y.[Shu-Yuan],
Cheng, L.[Lin],
Lu, Y.[Yang],
Wang, H.Z.[Han-Zi],
SCINet: Semantic Cue Infusion Network for Lane Detection,
ICIP22(1811-1815)
IEEE DOI
2211
Image segmentation, Lane detection, Semantics, Benchmark testing,
Robustness, Copper, Task analysis, Lane detection, self attention
BibRef
Wang, J.K.[Jing-Ke],
Ye, T.J.[Teng-Ju],
Gu, Z.Q.[Zi-Qing],
Chen, J.[Junbo],
LTP: Lane-based Trajectory Prediction for Autonomous Driving,
CVPR22(17113-17121)
IEEE DOI
2210
Tracking, Shape, Navigation, Prediction methods, Transformers,
Trajectory, Pattern recognition, Motion and tracking
BibRef
Wang, J.S.[Jin-Sheng],
Ma, Y.C.[Yin-Chao],
Huang, S.F.[Shao-Fei],
Hui, T.R.[Tian-Rui],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, T.Z.[Tian-Zhu],
A Keypoint-based Global Association Network for Lane Detection,
CVPR22(1382-1391)
IEEE DOI
2210
Correlation, Shape, Lane detection, Navigation, Estimation,
Benchmark testing, Segmentation, grouping and shape analysis,
Navigation and autonomous driving
BibRef
Zheng, T.[Tu],
Huang, Y.F.[Yi-Fei],
Liu, Y.[Yang],
Tang, W.J.[Wen-Jian],
Yang, Z.[Zheng],
Cai, D.[Deng],
He, X.F.[Xiao-Fei],
CLRNet: Cross Layer Refinement Network for Lane Detection,
CVPR22(888-897)
IEEE DOI
2210
Location awareness, Cross layer design, Visualization,
Lane detection, Navigation, Machine vision, Semantics,
Navigation and autonomous driving
BibRef
Yan, F.[Fan],
Nie, M.[Ming],
Cai, X.Y.[Xin-Yue],
Han, J.H.[Jian-Hua],
Xu, H.[Hang],
Yang, Z.[Zhen],
Ye, C.Q.[Chao-Qiang],
Fu, Y.W.[Yan-Wei],
Mi, M.B.[Michael Bi],
Zhang, L.[Li],
ONCE-3DLanes: Building Monocular 3D Lane Detection,
CVPR22(17122-17131)
IEEE DOI
2210
Point cloud compression, Technological innovation,
Lane detection, Annotations, Roads, Layout, 3D from single images
BibRef
Sato, T.[Takami],
Chen, Q.A.[Qi Alfred],
Towards Driving-Oriented Metric for Lane Detection Models,
CVPR22(17132-17141)
IEEE DOI
2210
Measurement, Lane detection, Navigation, Machine vision, Robustness,
Natural language processing, Pattern recognition,
Vision applications and systems
BibRef
Jin, D.[Dongkwon],
Park, W.[Wonhui],
Jeong, S.G.[Seong-Gyun],
Kwon, H.[Heeyeon],
Kim, C.S.[Chang-Su],
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse
Lanes,
CVPR22(17142-17150)
IEEE DOI
2210
Training, Image analysis, Codes, Navigation, Roads, Machine vision,
Navigation and autonomous driving,
Vision applications and systems
BibRef
Li, C.G.[Chen-Guang],
Zhang, B.[Boheng],
Shi, J.[Jia],
Cheng, G.L.[Guang-Liang],
Multi-level Domain Adaptation for Lane Detection,
WAD22(4379-4388)
IEEE DOI
2210
Costs, Lane detection, Shape, Image edge detection, Semantics
BibRef
Feng, Z.Y.[Zheng-Yang],
Guo, S.H.[Shao-Hua],
Tan, X.[Xin],
Xu, K.[Ke],
Wang, M.[Min],
Ma, L.Z.[Li-Zhuang],
Rethinking Efficient Lane Detection via Curve Modeling,
CVPR22(17041-17049)
IEEE DOI
2210
Deformable models, Convolutional codes, Image segmentation,
Lane detection, Detectors, Benchmark testing, Stability analysis,
Scene analysis and understanding
BibRef
Moujtahid, S.[Salma],
Benmokhtar, R.[Rachid],
Breheret, A.[Amaury],
Boukhdhir, S.E.[Saif-Eddine],
Spatial-UNet: Deep Learning-Based Lane Detection Using Fisheye Cameras
for Autonomous Driving,
CIAP22(II:576-586).
Springer DOI
2205
BibRef
Jayasinghe, O.[Oshada],
Hemachandra, S.[Sahan],
Anhettigama, D.[Damith],
Kariyawasam, S.[Shenali],
Rodrigo, R.[Ranga],
Jayasekara, P.[Peshala],
CeyMo: See More on Roads - A Novel Benchmark Dataset for Road Marking
Detection,
WACV22(3381-3390)
IEEE DOI
2202
Measurement, Image segmentation, Image resolution, Roads, Lighting,
Detectors, Benchmark testing, Datasets,
Vision for Aerial/Drone/Underwater/Ground Vehicles
BibRef
Jin, Y.J.[Yu-Jie],
Ren, X.X.[Xiang-Xuan],
Chen, F.X.[Feng-Xiang],
Zhang, W.D.[Wei-Dong],
Robust Monocular 3D Lane Detection With Dual Attention,
ICIP21(3348-3352)
IEEE DOI
2201
Interpolation, Correlation, Lane detection, Image processing,
Aggregates, 3D lane detection, attention mechanism
BibRef
Lin, Y.C.[Yan-Cong],
Pintea, S.L.[Silvia-Laura],
van Gemert, J.C.[Jan C.],
Semi-Supervised Lane Detection With Deep Hough Transform,
ICIP21(1514-1518)
IEEE DOI
2201
Lane detection, Annotations, Image processing, Neural networks,
Layout, Transforms, Lane detection, Hough Transform, semi-supervised learning
BibRef
Zhang, Z.H.[Zhi-Hua],
Zhang, X.X.[Xin-Xiu],
Yang, S.[Shuwen],
Yang, J.[Jun],
Research on Pavement Marking Recognition and Extraction Method,
ICIVC21(100-105)
IEEE DOI
2112
Maximum likelihood detection, Image segmentation,
Image edge detection, Roads, Nonlinear filters, Transforms,
marking extraction
BibRef
Meyer, A.[Annika],
Skudlik, P.[Philipp],
Pauls, J.H.[Jan-Hendrik],
Stiller, C.[Christoph],
YOLinO: Generic Single Shot Polyline Detection in Real Time,
AVVision21(2916-2925)
IEEE DOI
2112
Visualization, Image color analysis, Shape, Lane detection, Roads,
Urban areas, Object detection
BibRef
Shyam, P.[Pranjay],
Yoon, K.J.[Kuk-Jin],
Kim, K.S.[Kyung-Soo],
Weakly Supervised Approach for Joint Object and Lane Marking
Detection,
AVVision21(2885-2895)
IEEE DOI
2112
Performance evaluation, Training, Lane detection,
Image edge detection, Computer architecture, Object detection, Detectors
BibRef
Kim, B.D.[Byeoung-Do],
Park, S.H.[Seong Hyeon],
Lee, S.[Seokhwan],
Khoshimjonov, E.[Elbek],
Kum, D.[Dongsuk],
Kim, J.[Junsoo],
Kim, J.S.[Jeong Soo],
Choi, J.W.[Jun Won],
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of
Dynamic Agents,
CVPR21(14631-14640)
IEEE DOI
2111
Measurement, Dynamics, Semantics, Predictive models,
Feature extraction, Trajectory, Pattern recognition
BibRef
Narayanan, S.[Sriram],
Moslemi, R.[Ramin],
Pittaluga, F.[Francesco],
Liu, B.[Buyu],
Chandraker, M.[Manmohan],
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction,
CVPR21(15794-15803)
IEEE DOI
2111
Couplings, Context-aware services, Semantics, Tools,
Benchmark testing, Trajectory
BibRef
Qu, Z.[Zhan],
Jin, H.[Huan],
Zhou, Y.[Yang],
Yang, Z.[Zhen],
Zhang, W.[Wei],
Focus on Local: Detecting Lane Marker from Bottom Up via Key Point,
CVPR21(14117-14125)
IEEE DOI
2111
Training, Runtime, Shape, Training data, Predictive models,
Data models, Real-time systems
BibRef
Liu, R.J.[Rui-Jin],
Yuan, Z.J.[Ze-Jian],
Liu, T.[Tie],
Xiong, Z.L.[Zhi-Liang],
End-to-end Lane Shape Prediction with Transformers,
WACV21(3693-3701)
IEEE DOI
2106
Adaptation models, Shape, Lane detection, Roads, Process control,
Predictive models, Cameras
BibRef
Halfaoui, I.[Ibrahim],
Bouzaraa, F.[Fahd],
Urfalioglu, O.[Onay],
Li, M.Z.[Min-Zhen],
Real-time End-to-End Lane ID Estimation Using Recurrent Networks,
ICPR21(9304-9310)
IEEE DOI
2105
Location awareness, Visualization, Planing, Runtime, Roads, Semantics,
Estimation
BibRef
Cordes, K.[Kai],
Broszio, H.[Hellward],
Vehicle Lane Merge Visual Benchmark,
ICPR21(715-722)
IEEE DOI
2105
Location awareness, Visualization,
Target tracking, Shape, Trajectory planning, Benchmark testing
BibRef
Komori, H.[Hiroyuki],
Onoguchi, K.[Kazunori],
Lane Detection based on Object Detection and Image-to-image
Translation,
ICPR21(1075-1082)
IEEE DOI
2105
Image segmentation, Lane detection, Databases, Shape, Roads,
Object detection, Network architecture
BibRef
Chng, Z.M.[Zhe Ming],
Lew, J.M.H.[Joseph Mun Hung],
Lee, J.A.[Jimmy Addison],
RONELD: Robust Neural Network Output Enhancement for Active Lane
Detection,
ICPR21(6842-6849)
IEEE DOI
2105
Deep learning, Space vehicles, Lane detection,
Image edge detection, Roads, Neural networks, Real-time systems,
autonomous driving
BibRef
Ang, S.P.[Sui Paul],
Phung, S.L.[Son Lam],
Bouzerdoum, A.[Abdesselam],
Nguyen, T.N.A.[Thi Nhat Anh],
Duong, S.T.M.[Soan Thi Minh],
Schira, M.M.[Mark Matthias],
Real-time Pedestrian Lane Detection for Assistive Navigation using
Neural Architecture Search,
ICPR21(8392-8399)
IEEE DOI
2105
Image segmentation, Lane detection, Navigation, Neural networks,
Blindness, Tools, Real-time systems, Pedestrian lane detection,
deep learning
BibRef
Tabelini, L.[Lucas],
Berriel, R.[Rodrigo],
Paixão, T.M.[Thiago M.],
Badue, C.[Claudine],
de Souza, A.F.[Alberto F.],
Oliveira-Santos, T.[Thiago],
PolyLaneNet: Lane Estimation via Deep Polynomial Regression,
ICPR21(6150-6156)
IEEE DOI
2105
Measurement, Deep learning, Lane detection, Estimation, Cameras,
Real-time systems, Pattern recognition
BibRef
Garnett, N.[Noa],
Uziel, R.[Roy],
Efrat, N.[Netalee],
Levi, D.[Dan],
Synthetic-to-real Domain Adaptation for Lane Detection,
ACCV20(VI:52-67).
Springer DOI
2103
BibRef
Tian, J.,
Yuan, J.,
Liu, H.,
Road Marking Detection Based on Mask R-CNN Instance Segmentation
Model,
CVIDL20(246-249)
IEEE DOI
2102
feature extraction, image segmentation,
learning (artificial intelligence), object detection,
traffic scene
BibRef
Qin, Z.[Zequn],
Wang, H.Y.[Huan-Yu],
Li, X.[Xi],
Ultra Fast Structure-aware Deep Lane Detection,
ECCV20(XXIV:276-291).
Springer DOI
2012
BibRef
Wang, B.K.[Bing-Ke],
Wang, Z.L.[Zi-Lei],
Zhang, Y.X.[Yi-Xin],
Polynomial Regression Network for Variable-number Lane Detection,
ECCV20(XVIII:719-734).
Springer DOI
2012
BibRef
Saqib, A.,
Sajid, S.,
Arif, S.M.,
Tariq, A.,
Ashraf, N.,
Domain Adaptation For Lane Marking: An Unsupervised Approach,
ICIP20(2381-2385)
IEEE DOI
2011
Decoding, Adaptation models, Image segmentation, Roads,
Machine learning, Training, Mathematical model, Domain Adaptation,
Convolutional Neural Network (CNN)
BibRef
Guo, Y.L.[Yu-Liang],
Chen, G.[Guang],
Zhao, P.[Peitao],
Zhang, W.[Weide],
Miao, J.[Jinghao],
Wang, J.[Jingao],
Choe, T.E.[Tae Eun],
GEN-Lanenet: A Generalized and Scalable Approach for 3d Lane Detection,
ECCV20(XXI:666-681).
Springer DOI
2011
BibRef
Hou, Y.,
Ma, Z.,
Liu, C.,
Hui, T.,
Loy, C.C.,
Inter-Region Affinity Distillation for Road Marking Segmentation,
CVPR20(12483-12492)
IEEE DOI
2008
Roads, Task analysis, Computational modeling, Image segmentation,
Training, Feature extraction, Image edge detection
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Yoo, S.,
Lee, H.S.[H. Seok],
Myeong, H.,
Yun, S.,
Park, H.,
Cho, J.,
Kim, D.H.[D. Hoon],
End-to-End Lane Marker Detection via Row-wise Classification,
AutoDrive20(4335-4343)
IEEE DOI
2008
Task analysis, Image segmentation, Computer architecture,
Semantics, Visualization, Spatial resolution, Computational complexity
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Philion, J.[Jonah],
FastDraw: Addressing the Long Tail of Lane Detection by Adapting a
Sequential Prediction Network,
CVPR19(11574-11583).
IEEE DOI
2002
BibRef
Chougule, S.[Shriyash],
Koznek, N.[Nora],
Ismail, A.[Asad],
Adam, G.[Ganesh],
Narayan, V.[Vikram],
Schulze, M.[Matthias],
Reliable Multilane Detection and Classification by Utilizing CNN as a
Regression Network,
ApolloScape18(V:740-752).
Springer DOI
1905
BibRef
Ghafoorian, M.[Mohsen],
Nugteren, C.[Cedric],
Baka, N.[Nóra],
Booij, O.[Olaf],
Hofmann, M.[Michael],
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane
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CVRoads18(I:256-272).
Springer DOI
1905
BibRef
Jung, J.[Jaehoon],
Che, E.[Erzhuo],
Olsen, M.J.[Michael J.],
Parrish, C.[Christopher],
Efficient and robust lane marking extraction from mobile lidar point
clouds,
PandRS(147), 2019, pp. 1-18.
Elsevier DOI
1901
Mobile laser scanning, Point cloud, Lane marking extraction
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Roberts, B.[Brook],
Kaltwang, S.[Sebastian],
Samangooei, S.[Sina],
Pender-Bare, M.[Mark],
Tertikas, K.[Konstantinos],
Redford, J.[John],
A Dataset for Lane Instance Segmentation in Urban Environments,
ECCV18(VIII: 543-559).
Springer DOI
1810
BibRef
Zhang, J.[Jie],
Xu, Y.[Yi],
Ni, B.B.[Bing-Bing],
Duan, Z.Y.[Zhen-Yu],
Geometric Constrained Joint Lane Segmentation and Lane Boundary
Detection,
ECCV18(I: 502-518).
Springer DOI
1810
BibRef
Lee, S.,
Kim, J.,
Yoon, J.S.,
Shin, S.,
Bailo, O.,
Kim, N.,
Lee, T.H.,
Hong, H.S.,
Han, S.H.,
Kweon, I.S.,
VPGNet: Vanishing Point Guided Network for Lane and Road Marking
Detection and Recognition,
ICCV17(1965-1973)
IEEE DOI
1802
feature extraction, image colour analysis,
image recognition, object detection, rain, road vehicles, roads,
Roads
BibRef
Bailo, O.,
Lee, S.,
Rameau, F.,
Yoon, J.S.,
Kweon, I.S.,
Robust Road Marking Detection and Recognition Using Density-Based
Grouping and Machine Learning Techniques,
WACV17(760-768)
IEEE DOI
1609
Cameras, Feature extraction, Image edge detection, Lighting,
Neural networks, Roads, Robustness
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Celenk, M.[Mehmet],
Riley, H.B.[H. Bryan],
Performance Assessment of Predictive Lane Boundary Detection for
Non-uniformly Illuminated Roadway Driving Assistance,
CRV16(170-177)
IEEE DOI
1612
Driving Assistance Roadway Features
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Kim, H.S.[Hee-Soo],
Beak, S.H.[Seung-Hae],
Park, S.Y.[Soon-Yong],
Parallel Hough Space Image Generation Method for Real-Time Lane
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ACIVS16(81-91).
Springer DOI
1611
BibRef
Takahashi, G.,
Takeda, H.,
Nakamura, K.,
Drawing for Traffic Marking Using Bidirectional Gradient-based
Detection with MMS Lidar Intensity,
ISPRS16(B5: 725-732).
DOI Link
1610
BibRef
Saussard, R.[Romain],
Bouzid, B.[Boubker],
Vasiliu, M.[Marius],
Reynaud, R.[Roger],
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ICIP15(4694-4697)
IEEE DOI
1512
ADAS; Embedded Processing; Lane Detection
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Chen, T.[Tao],
Lu, S.J.[Shi-Jian],
Context-aware lane marking detection on urban roads,
ICIP15(2557-2561)
IEEE DOI
1512
Lane marking; MSER; context-aware features; detection
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Teng, S.Y.[Shan-Yun],
Chuang, K.T.[Kun-Ta],
Huang, C.R.[Chun-Rong],
Li, C.C.[Cheng-Chun],
Lane detection in surveillance videos using vector-based hierarchy
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MVA15(345-348)
IEEE DOI
1507
Global Positioning System
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Lai, S.H.[Shang-Hong],
Integrated Vehicle and Lane Detection with Distance Estimation,
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Springer DOI
1504
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Li, C.[Cheng],
Creusen, I.[Ivo],
Hazelhoff, L.[Lykele],
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Detection and Recognition of Road Markings in Panoramic Images,
IVVT14(448-458).
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1504
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Mazurek, P.[Przemyslaw],
Shallow Convolutional Neural Network and Viterbi Algorithm for Dim Line
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ICCVG18(375-384).
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1810
BibRef
And:
Directional Filter and the Viterbi Algorithm for Line Following Robots,
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1410
BibRef
Aly, M.,
Real time detection of lane markers in urban streets,
IVS08(7-12).
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BibRef
0800
Truong, Q.B.[Quoc Bao],
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Heo, N.G.[Nam Geon],
Yum, Y.J.[Young Jim],
Kim, J.G.[Jong Gook],
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IEEE DOI
1109
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Labayrade, R.,
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1109
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Liu, X.[Xin],
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See also Visual Saliency Based on Scale-Space Analysis in the Frequency Domain.
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IEEE DOI
1109
BibRef
Li, J.[Jian],
An, X.J.[Xiang-Jing],
He, H.[Hangen],
Lane Detection Based on Visual Attention,
ICIG11(570-575).
IEEE DOI
1109
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Shang, E.[Erke],
Li, J.[Jian],
An, X.J.[Xiang-Jing],
He, H.[Hangen],
Lane Detection Using Steerable Filters and FPGA-based Implementation,
ICIG11(908-913).
IEEE DOI
1109
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Ben Romdhane, N.[Nadra],
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Springer DOI
1108
See also Comparative Study of Vision-Based Traffic Signs Recognition Methods, A.
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Ammar, M.[Moez],
Le Hégarat-Mascle, S.[Sylvie],
Mounier, H.[Hugues],
Road Surface Marking Classification Based on a Hierarchical Markov
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ICIAR11(II: 348-359).
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1106
See also A-contrario Approach for Obstacle Detection in Assistance Driving Systems, An.
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Hara, T.[Takayuki],
Aoki, Y.[Yoshimitsu],
A robust road marking detection method with pseudo-top-view images for
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IEEE DOI
1102
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Wong, D.,
Deguchi, D.[Daisuke],
Ide, I.[Ichiro],
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CVRoads15(90-97)
IEEE DOI
1602
Cameras
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Noda, M.[Masafumi],
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Deguchi, D.[Daisuke],
Ide, I.[Ichiro],
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Kojima, Y.[Yoshiko],
Naito, T.[Takashi],
Vehicle Ego-Localization by Matching In-Vehicle Camera Images to an
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1109
BibRef
Earlier:
Recognition of Road Markings from In-Vehicle Camera Images by a
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MVA09(514-).
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Cheng, G.,
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Qian, Y.,
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IEEE DOI
2006
Road Surface Segmentation, Domain Adaptation, Transfer Learning
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Cheng, G.,
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1004
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Borkar, A.[Amol],
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A Template Matching and Ellipse Modeling Approach to Detecting Lane
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ACIVS10(II: 179-190).
Springer DOI
1012
BibRef
Earlier:
Robust lane detection and tracking with ransac and Kalman filter,
ICIP09(3261-3264).
IEEE DOI
0911
BibRef
And:
Lane Detection and Tracking Using a Layered Approach,
ACIVS09(474-484).
Springer DOI
0909
BibRef
Shi, X.J.[Xue-Jie],
Kong, B.[Bin],
Zheng, F.[Fei],
A New Lane Detection Method Based on Feature Pattern,
CISP09(1-5).
IEEE DOI
0910
BibRef
Gao, T.[Tianshi],
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Self lane assignment using egocentric smart mobile camera for
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Egocentric09(57-62).
IEEE DOI
0906
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Su, C.Y.[Chung-Yen],
Fan, G.H.[Gen-Hau],
An Effective and Fast Lane Detection Algorithm,
ISVC08(II: 942-948).
Springer DOI
0812
BibRef
Kang, Y.[Yousun],
Yamaguchi, K.[Koichiro],
Naito, T.[Takashi],
Ninomiya, Y.[Yoshiki],
Road Image Segmentation and Recognition Using Hierarchical
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PSIVT11(I: 248-256).
Springer DOI
1111
See also Feature Interaction Descriptor for Pedestrian Detection.
BibRef
Yamaguchi, K.[Koichiro],
Watanabe, A.[Akihiro],
Naito, T.[Takashi],
Ninomiya, Y.[Yoshiki],
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IEEE DOI
0812
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Ninomiya, Y.[Yoshiki],
3D Line Reconstruction of a Road Environment Using an In-Vehicle Camera,
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Springer DOI
0812
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Lipski, C.[Christian],
Scholz, B.[Bjorn],
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0803
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López, A.M.[Antonio M.],
Serrat, J.[Joan],
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Robust Lane Lines Detection and Quantitative Assessment,
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0706
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Labayrade, R.,
How Autonomous Mapping Can Help a Road Lane Detection System ?,
ICARCV06(1-6).
IEEE DOI
0612
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Samadzadegan, F.,
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Tabibi, M.,
Automatic lane detection in image sequences for vision based navigation
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IEVM06(xx-yy).
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Roncella, R.,
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Automatic lane parameters extraction in mobile mapping sequences,
IEVM06(xx-yy).
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0609
BibRef
Dupuis, J.,
Parizeau, M.,
Evolving a Vision-Based Line-Following Robot Controller,
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0607
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Khelifi, S.F.,
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ICIAR04(II: 563-571).
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0409
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Cramer, H.,
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Wanielik, G.,
A new approach for tracking lanes by fusing image measurements with map
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IVS04(607-612).
IEEE DOI
0411
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Dal Poz, A.P.[Aluir Porfírio],
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Active Testing and Edge Analysis for Road Centreline Extraction,
PCV02(B: 44).
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Toth, C.K.[Charles K.],
Grejner-Brzezinska, D.A.[Dorota A.],
Near Real-Time Road Centerline Extraction,
PCV02(A: 362).
0305
BibRef
Muck, K.,
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Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Lane Changing, Lane-Change, Analysis, Control .