15.3.3.3 Lane Detection, Lane Following, White Line Detection

mSonar,
2011
WWW Link. Vendor, Lane Following. 1106
Phone app to follow lanes using camera phone. Applied to navigation. From Lustancia LTD.

Barnes, R.R.[Robert R.], Myers, C.L.[Charles L.],
Driver alert system,
US_Patent4,348,652, Sep 7, 1982
WWW Link. Line detection using IR illumination. BibRef 8209

Tsuchihashi, T.[Toshio],
Vehicle for driving along a lead line,
US_Patent4,588,041, May 13, 1986
WWW Link. BibRef 8605

Yamamoto, K.[Kenzo],
Method and apparatus for controlling travel of an automatic guided vehicle,
US_Patent4,593,239, Jun 3, 1986
WWW Link. BibRef 8606

Swenson, R.R.[Ronald R.],
Floor position sensing apparatus and method,
US_Patent4,613,804, Sep 23, 1986
WWW Link. follow marked path BibRef 8609

Barton, R.R.[Russell R.],
Vehicle tracking system,
US_Patent4,630,109, Dec 16, 1986
WWW Link. Follow distinct white stripe. BibRef 8612

Kenue, S.K.,
LANELOK: Detection of Lane Boundaries and Vehicle Tracking Using Image-Processing Techniques, Part I: Hough-Transform Region-Tracing and Correlation Algorithms,

And:
Part II: Template Matching Algorithms,
GM ResearchTR, November 1989. BibRef 8911
And: Both are in: SPIE(1195), November 1989. Report on GM effort in road detection, find likely boundaries, track through the frames. BibRef

Kenue, S.K., Bajpayee, S.,
LaneLok: Robust Line and Curve Fitting of Lane Boundaries,
SPIE(1831), 1990, pp. 491-503. Boston. BibRef 9000

Kenue, S.K.[Surender K.],
Vision method of detecting lane boundaries and obstacles,
US_Patent4,970,653, Nov 13, 1990
WWW Link. BibRef 9011
And:
LANELOK: An Algorithm for Extending the Lane Sensing Operating Range to 100 Feet,
SPIE(1388), November 1990. BibRef
And: GM ResearchLabs TR. BibRef

Ogawa, H.[Hiroshi], Miyazaki, Y.[Yasuyuki], Yoshida, S.[Susumu],
Unmanned vehicle control system and method,
US_Patent4,665,487, May 12, 1987
WWW Link. BibRef 8705

Ishikawa, S., Kuwamoto, H., and Ozawa, S.,
Visual Navigation of an Autonomous Vehicle Using White Line Recognition,
PAMI(10), No. 5, September 1988, pp. 743-749.
IEEE DOI Follow the white stripe. Simple processing, but with collision avoidance. all visual control. BibRef 8809

Ishikawa, S., Ozawa, S.,
A Method of Image Guided Vehicle Using White Line Recognition,
CVPR86(47-53). White stripe following. BibRef 8600

Mathur, B.P.[Bimal P.], Wang, H.T.[H. Taichi], Haendel, R.S.[Richard S.],
Vehicle lane position detection system,
US_Patent5,351,044, Sep 27, 1994
WWW Link. BibRef 9409

Chen, K.H., Tsai, W.H.,
Vision-Based Autonomous Land Vehicle Guidance in Outdoor Road Environments Using Combined Line and Road Following Techniques,
JRS(14), No. 10, October 1997, pp. 711-728. 9710
BibRef

Chen, G.Y., Tsai, W.H.,
An Incremental-Learning-by-Navigation Approach to Vision-Based Autonomous Land Vehicle Guidance in Indoor Environments Using Vertical Line Information and Multiweighted Generalized Hough Transform Technique,
SMC-B(28), No. 5, October 1998, pp. 740-748.
IEEE Top Reference. 9810
BibRef

Guiducci, A.[Antonio],
Camera Calibration for Road Applications,
CVIU(79), No. 2, August 2000, pp. 250-266.
DOI Link 0008
BibRef

Guiducci, A.[Antonio],
Parametric Model of the Perspective Projection of a Road with Applications to Lane Keeping and 3D Road Reconstruction,
CVIU(73), No. 3, March 1999, pp. 414-427.
DOI Link BibRef 9903

Goldbeck, J., Huertgen, B., Ernst, S., Kelch, L.,
Lane following combining vision and DGPS,
IVC(18), No. 5, April 2000, pp. 425-433.
Elsevier DOI 0003
BibRef

Lai, A.H.S., Yung, N.H.C.,
Lane Detection by Orientation and Length Discrimination,
SMC-B(30), No. 4, August 2000, pp. 539-548.
IEEE Top Reference. 0008
BibRef

Wang, Y.[Yue], Shen, D.G.[Ding-Gang], Teoh, E.K.[Eam Khwang],
Lane detection using spline model,
PRL(21), No. 6-7, June 2000, pp. 677-689. 0006
BibRef

Wang, Y.[Yue], Teoh, E.K.[Eam Khwang], Shen, D.G.[Ding-Gang],
Lane detection and tracking using B-Snake,
IVC(22), No. 4, 1 April 2004, pp. 269-280.
Elsevier DOI 0402
BibRef

Wang, Y., Shen, D., Teoh, E.K., Wang, H.,
A Novel Lane Model for Lane Boundary Detection,
MVA98(xx-yy). BibRef 9800

Beauvais, M., Lakshmanan, S.,
CLARK: a heterogeneous sensor fusion method for finding lanes and obstacles,
IVC(18), No. 5, April 2000, pp. 397-413.
Elsevier DOI 0003
BibRef

Ma, B.[Bing], Lakshmanan, S.[Sridhar], Hero, III, A.O.[Alfred O.],
Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion,
ITS(1), No. 3, September 2000, pp. 135-147.
IEEE Abstract. 0402
BibRef
Earlier:
Road and lane Edge detection with Multisensor Fusion Methods,
ICIP99(II:686-690).
IEEE DOI BibRef

Yasui, N.[Nobuhiko], Iisaka, A.[Atsushi], Kaneko, M.[Mamoru],
Local positioning apparatus, and a method therefor,
US_Patent6,091,833, Jul 18, 2000
WWW Link. Lane position. BibRef 0007

Park, J.W.[Jong Woung], Lee, J.W.[Joon Woong], Jhang, K.Y.[Kyung Young],
A Lane-Curve Detection Based on an LCF,
PRL(24), No. 14, October 2003, pp. 2301-2313.
Elsevier DOI 0307
BibRef

Kang, D.J.[Dong-Joong], Jung, M.H.[Mun-Ho],
Road lane segmentation using dynamic programming for active safety vehicles,
PRL(24), No. 16, December 2003, pp. 3177-3185.
Elsevier DOI 0310
BibRef

Yim, Y.U.[Young Uk], Oh, S.Y.[Se-Young],
Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving,
ITS(4), No. 4, December 2003, pp. 219-225.
IEEE Abstract. 0402
BibRef

Li, Q.[Qing], Zheng, N.N.[Nan-Ning], Cheng, H.[Hong],
Springrobot: A Prototype Autonomous Vehicle and its Algorithms for Lane Detection,
ITS(5), No. 4, December 2004, pp. 300-308.
IEEE Abstract. 0501
BibRef

Wang, J., Schroedl, S., Mezger, K., Ortloff, R., Joos, A., Passegger, T.,
Lane Keeping Based on Location Technology,
ITS(6), No. 3, September 2005, pp. 351-356.
IEEE DOI 0509
BibRef

Choi, S.Y.[Sung Yug], Lee, J.M.[Jang Myung],
Applications of moving windows technique to autonomous vehicle navigation,
IVC(24), No. 2, 1 February 2006, pp. 120-130.
Elsevier DOI 0604
Moving window; Lane detection; Obstacle detection; Mobile robot; Corridor driving BibRef

Cheng, H.Y., Jeng, B.S., Tseng, P.T., Fan, K.C.,
Lane Detection With Moving Vehicles in the Traffic Scenes,
ITS(7), No. 4, December 2006, pp. 571-582.
IEEE DOI 0701
BibRef

Zhang, Q., Couloigner, I.,
Accurate Centerline Detection and Line Width Estimation of Thick Lines Using the Radon Transform,
IP(16), No. 2, February 2007, pp. 310-316.
IEEE DOI 0702
BibRef

Dao, T.S., Leung, K.Y.K., Clark, C.M., Huissoon, J.P.,
Markov-Based Lane Positioning Using Intervehicle Communication,
ITS(8), No. 4, December 2007, pp. 641-650.
IEEE DOI 0712
BibRef

Tan, H.S., Bu, F., Bougler, B.,
A Real-World Application of Lane-Guidance Technologies: Automated Snowblower,
ITS(8), No. 3, September 2007, pp. 538-548.
IEEE DOI 0710
BibRef

Wu, S.J., Chiang, H.H., Perng, J.W., Chen, C.J., Wu, B.F., Lee, T.T.,
The Heterogeneous Systems Integration Design and Implementation for Lane Keeping on a Vehicle,
ITS(9), No. 2, June 2008, pp. 246-263.
IEEE DOI 0806
BibRef

Danescu, R.G., Nedevschi, S.,
Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision,
ITS(10), No. 2, June 2009, pp. 272-282.
IEEE DOI 0906
BibRef

Jeong, P.Y.[Pang-Yu], Nedevschi, S.,
Efficient and Robust Classification Method Using Combined Feature Vector for Lane Detection,
CirSysVideo(15), No. 4, April 2005, pp. 528-537.
IEEE Abstract. 0501
BibRef
Earlier:
Efficient Classification Method for Autonomous Driving Application,
ICIAR04(I: 228-235).
Springer DOI 0409
BibRef
Earlier:
Intelligent road detection based on local averaging classifier in real-time environments,
CIAP03(245-249).
IEEE DOI 0310
BibRef

Jeong, P.Y.[Pang-Yu], Nedevschi, S.,
Local Difference Probability (LDP)-Based Environment Adaptive Algorithm for Unmanned Ground Vehicle,
ITS(7), No. 3, September 2006, pp. 282-292.
IEEE DOI 0609
BibRef

Jeong, P.Y.[Pang-Yu], Nedevschi, S., Daniliuc, M.,
Local probability based safe region detection for autonomous driving,
IVS04(744-749).
IEEE DOI 0411
BibRef

Chiang, H.H.[Hsin-Han], Wu, S.J.[Shinq-Jen], Perng, J.W.[Jau-Woei], Wu, B.F.[Bing-Fei], Lee, T.T.[Tsu-Tian],
The Human-in-the-Loop Design Approach to the Longitudinal Automation System for an Intelligent Vehicle,
SMC-A(40), No. 4, July 2010, pp. 708-720.
IEEE DOI 1007
BibRef

Kim, Z.[Zu_Whan],
Robust Lane Detection and Tracking in Challenging Scenarios,
ITS(9), No. 1, March 2008, pp. 16-26.
IEEE DOI 0803
BibRef
Earlier:
Realtime Road Detection by Learning from One Example,
WACV05(I: 455-460).
IEEE DOI 0502
BibRef

Du, J., Barth, M.J.,
Next-Generation Automated Vehicle Location Systems: Positioning at the Lane Level,
ITS(9), No. 1, March 2008, pp. 48-57.
IEEE DOI 0803
BibRef

Hassouna, M.S.[M. Sabry], Abdel-Hakim, A.E.[Alaa E.], Farag, A.A.[Aly A.],
PDE-Based Robust Robotic Navigation,
IVC(27), No. 1-2, January 2009, pp. 10-18.
Elsevier DOI 0804
BibRef
Earlier: CRV05(176-183).
IEEE DOI 0505
BibRef
And:
Robust Robotic Path Planning Using Level Sets,
ICIP05(III: 473-476).
IEEE DOI 0512

See also Image content-based active sensor planning for a mobile trinocular active vision system. Robotic navigation; Level set methods; Fast marching methods; Path planning; Optimum path; Skeletonization BibRef

Farag, A.A.[Aly A.], Abdel-Hakim, A.E.[Alaa E.],
Virtual Forces for Camera Planning in Smart Vision Systems,
WACV05(I: 269-274).
IEEE DOI 0502
BibRef

Hassouna, M.S.[M. Sabry], Farag, A.A.[Aly A.],
Robust Centerline Extraction Framework Using Level Sets,
CVPR05(I: 458-465).
IEEE DOI 0507
BibRef

Cheng, H.Y., Yu, C.C., Tseng, C.C., Fan, K.C., Hwang, J.N., Jeng, B.S.,
Environment classification and hierarchical lane detection for structured and unstructured roads,
IET-CV(4), No. 1, March 2010, pp. 37-49.
DOI Link 1001
BibRef

Jiang, R.[Ruyi], Klette, R.[Reinhard], Vaudrey, T.[Tobi], Wang, S.G.[Shi-Gang],
Lane detection and tracking using a new lane model and distance transform,
MVA(22), No. 4, July 2011, pp. 721-737.
WWW Link. 1107
BibRef
Earlier:
New Lane Model and Distance Transform for Lane Detection and Tracking,
CAIP09(1044-1052).
Springer DOI 0909
BibRef

Xiao, J.S.[Jin-Sheng], Luo, L.[Li], Yao, Y.[Yuan], Zou, W.T.[Wen-Tao], Klette, R.[Reinhard],
Lane Detection Based on Road Module and Extended Kalman Filter,
PSIVT17(382-395).
Springer DOI 1802
BibRef

Amditis, A., Bimpas, M., Thomaidis, G., Tsogas, M., Netto, M., Mammar, S., Beutner, A., Mohler, N., Wirthgen, T., Zipser, S., Etemad, A., da Lio, M., Cicilloni, R.,
A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach,
ITS(11), No. 3, September 2010, pp. 617-629.
IEEE DOI 1003
BibRef

Nieto, M.[Marcos], Arróspide-Laborda, J.[Jon], Salgado, L.[Luis],
Road environment modeling using robust perspective analysis and recursive Bayesian segmentation,
MVA(22), No. 6, November 2011, pp. 927-945.
WWW Link. 1110
BibRef

Nieto, M.[Marcos], Salgado, L.[Luis], Jaureguizar, F.[Fernando], Arróspide-Laborda, J.[Jon],
Robust multiple lane road modeling based on perspective analysis,
ICIP08(2396-2399).
IEEE DOI 0810
BibRef

Kim, J.Y.[Ju-Young], Jang, J.S.[Ja-Soon],
A simple model for a lane detection system,
SPIE(Newsroom), February 15, 2012.
DOI Link 1202
An efficient algorithm for detecting lane markers uses the log-polar transform and random sample consensus to achieve robust performance under various road conditions. BibRef

Kim, J.Y., Lim, H.R., Lee, C.S., Jang, J.S.,
An efficient lane markers detection algorithm using log-polar transform and RANSAC,
SPIE(8135), 2011, pp. 81351J.
DOI Link BibRef 1100

Gikas, V., Stratakos, J.,
A Novel Geodetic Engineering Method for Accurate and Automated Road/Railway Centerline Geometry Extraction Based on the Bearing Diagram and Fractal Behavior,
ITS(13), No. 1, March 2012, pp. 115-126.
IEEE DOI 1203
BibRef

Borkar, A., Hayes, M., Smith, M.T.,
A Novel Lane Detection System With Efficient Ground Truth Generation,
ITS(13), No. 1, March 2012, pp. 365-374.
IEEE DOI 1203
BibRef

Cameron, M.[Mark], Al-Bahadly, I.[Ibrahim], Zakaria, Z.[Zulkarnay], Ayob, N.M.N.[Nor Muzakkir Nor],
A Line Detection Algorithm for Road Remarking,
Sensors(140), No. 5, May 2012, pp. 65-73.
HTML Version. BibRef 1205

Wu, C.F., Lin, C.J., Lee, C.Y.,
Applying a Functional Neurofuzzy Network to Real-Time Lane Detection and Front-Vehicle Distance Measurement,
SMC-C(42), No. 4, July 2012, pp. 577-589.
IEEE DOI 1206
BibRef

Fang, Y.F.[Yun-Fei], Chu, F.[Feng], Mammar, S., Zhou, M.[MengChu],
Optimal Lane Reservation in Transportation Network,
ITS(13), No. 2, June 2012, pp. 482-491.
IEEE DOI 1206
Not land detection. BibRef

Vu, A.[Anh], Ramanandan, A., Chen, A.[Anning], Farrell, J.A., Barth, M.,
Real-Time Computer Vision/DGPS-Aided Inertial Navigation System for Lane-Level Vehicle Navigation,
ITS(13), No. 2, June 2012, pp. 899-913.
IEEE DOI 1206
BibRef

Demcenko, A., Tamosiunaite, M., Vidugiriene, A., Jakevicius, L.,
Estimation of Lane Marker Parameters With High Correlation to Steering Signal,
ITS(13), No. 2, June 2012, pp. 962-967.
IEEE DOI 1206
BibRef

Gopalan, R., Hong, T., Shneier, M., Chellappa, R.,
A Learning Approach Towards Detection and Tracking of Lane Markings,
ITS(13), No. 3, September 2012, pp. 1088-1098.
IEEE DOI 1209
BibRef

Alam, N., Balaei, A.T., Dempster, A.G.,
An Instantaneous Lane-Level Positioning Using DSRC Carrier Frequency Offset,
ITS(13), No. 4, December 2012, pp. 1566-1575.
IEEE DOI 1212
BibRef

Liu, G., Worgotter, F., Markelic, I.,
Stochastic Lane Shape Estimation Using Local Image Descriptors,
ITS(14), No. 1, March 2013, pp. 13-21.
IEEE DOI 1303
BibRef

Li, X.Y.[Xiang-Yang], Fang, X.Z.[Xiang-Zhong],
Parallel-Snake with Balloon Force for Lane Detection,
IEICE(E97-D), No. 2, February 2013, pp. 349-352.
WWW Link. 1402
BibRef

Wu, P.C.[Pei-Chen], Chang, C.Y.[Chin-Yu], Lin, C.H.[Chang Hong],
Lane-mark extraction for automobiles under complex conditions,
PR(47), No. 8, 2014, pp. 2756-2767.
Elsevier DOI 1405
Line fitting BibRef

Shin, B.S.[Bok-Suk], Xu, Z.Z.[Ze-Zhong], Klette, R.[Reinhard],
Visual lane analysis and higher-order tasks: a concise review,
MVA(25), No. 6, 2014, pp. 1519-1547.
WWW Link. 1408
BibRef

Shin, B.S.[Bok-Suk], Tao, J.L.[Jun-Li], Klette, R.[Reinhard],
A superparticle filter for lane detection,
PR(48), No. 11, 2015, pp. 3333-3345.
Elsevier DOI 1506
BibRef
Earlier: A2, A1, A3:
Wrong Roadway Detection for Multi-lane Roads,
CAIP13(II:50-58).
Springer DOI 1311
Lane model BibRef

de Paula, M.B., Jung, C.R.,
Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras,
ITS(16), No. 6, December 2015, pp. 3160-3169.
IEEE DOI 1512
Bayes methods BibRef

Jung, S., Youn, J., Sull, S.,
Efficient Lane Detection Based on Spatiotemporal Images,
ITS(17), No. 1, January 2016, pp. 289-295.
IEEE DOI 1601
Image edge detection BibRef

Nieto, M.[Marcos], Cortés, A.[Andoni], Otaegui, O.[Oihana], Arróspide, J.[Jon], Salgado, L.[Luis],
Real-time lane tracking using Rao-Blackwellized particle filter,
RealTimeIP(11), No. 1, January 2016, pp. 179-191.
Springer DOI 1601
BibRef

Du, X.X.[Xin-Xin], Tan, K.K.[Kok Kiong],
Vision-based approach towards lane line detection and vehicle localization,
MVA(27), No. 2, February 2016, pp. 175-191.
WWW Link. 1602
BibRef

Cui, D., Xue, J., Zheng, N.,
Real-Time Global Localization of Robotic Cars in Lane Level via Lane Marking Detection and Shape Registration,
ITS(17), No. 4, April 2016, pp. 1039-1050.
IEEE DOI 1604
Accuracy BibRef

Du, X., Tan, K.K.,
Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization,
IP(25), No. 5, May 2016, pp. 2075-2088.
IEEE DOI 1604
image motion analysis BibRef

Niu, J.W.[Jian-Wei], Lu, J.[Jie], Xu, M.L.[Ming-Liang], Lv, P.[Pei], Zhao, X.[Xiaoke],
Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting,
PR(59), No. 1, 2016, pp. 225-233.
Elsevier DOI 1609
Lane detection BibRef

Das, A., Srinivasa Murthy, S., Suddamalla, U.,
Enhanced Algorithm of Automated Ground Truth Generation and Validation for Lane Detection System by M^2 BMT,
ITS(18), No. 4, April 2017, pp. 996-1005.
IEEE DOI 1704
modified min-between-max thresholding BibRef

Cheng, G., Wang, Y., Xu, S., Wang, H., Xiang, S., Pan, C.,
Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network,
GeoRS(55), No. 6, June 2017, pp. 3322-3337.
IEEE DOI 1706
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 BibRef

Piao, J.C.[Jing-Chun], Shin, H.C.[Hyun-Chul],
Robust hypothesis generation method using binary blob analysis for multi-lane detection,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1210-1218.
DOI Link 1712
BibRef

Yoo, J.H., Lee, S.W., Park, S.K., Kim, D.H.,
A Robust Lane Detection Method Based on Vanishing Point Estimation Using the Relevance of Line Segments,
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 Low-Cost Approach for Autonomous Driving,
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
BibRef

John, V.[Vijay], Liu, Z.[Zheng], Mita, S.[Seiichi], Guo, C.Z.[Chun-Zhao], Kidono, K.[Kiyosumi],
Real-time road surface and semantic lane estimation using deep features,
SIViP(12), No. 6, September 2018, pp. 1133-1140.
WWW Link. 1808
BibRef
Earlier: A1, A2, A4, A3, A5:
Real-Time Lane Estimation Using Deep Features and Extra Trees Regression,
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 BibRef

Park, J.M.[Jeong Min], Lee, J.W.[Joon Woong],
Lane estimation by particle-filtering combined with likelihood computation of line boundaries and motion compensation,
IVC(79), 2018, pp. 11-24.
Elsevier DOI 1811
Probabilistic lane estimation, Likelihood computation, Motion compensation, ROI weighting, Two-step particle filtering BibRef

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 BibRef

Xing, Y.[Yang], Lv, C.[Chen], Wang, H.J.[Hua-Ji], Cao, D.P.[Dong-Pu], Velenis, E.[Efstathios],
Dynamic integration and online evaluation of vision-based lane detection algorithms,
IET-ITS(13), No. 1, January 2019, pp. 55-62.
DOI Link 1901
BibRef

Wan, R.[Rui], Huang, Y.C.[Yu-Chun], Xie, R.C.[Rong-Chang], Ma, P.[Ping],
Combined Lane Mapping Using a Mobile Mapping System,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Son, Y.[Yeongho], Lee, E.S.[Elijah S.], Kum, D.[Dongsuk],
Robust multi-lane detection and tracking using adaptive threshold and lane classification,
MVA(30), No. 1, February 2019, pp. 111-124.
WWW Link. 1904
BibRef

Ozgunalp, U.[Umar],
Robust lane-detection algorithm based on improved symmetrical local threshold for feature extraction and inverse perspective mapping,
IET-IPR(13), No. 6, 10 May 2019, pp. 975-982.
DOI Link 1906
BibRef

Küçükmanisa, A.[Ayhan], Akbulut, O.[Orhan], Urhan, O.[Oguzhan],
Robust and real-time lane detection filter based on adaptive neuro-fuzzy inference system,
IET-IPR(13), No. 7, 30 May 2019, pp. 1181-1190.
DOI Link 1906
BibRef

Guan, J.G.[Jun-Gang], An, F.W.[Feng-Wei], Zhang, X.Y.[Xiang-Yu], Chen, L.[Lei], Mattausch, H.J.[Hans Jürgen],
Energy-Efficient Hardware Implementation of Road-Lane Detection Based on Hough Transform with Parallelized Voting Procedure and Local Maximum Algorithm,
IEICE(E102-D), No. 6, June 2019, pp. 1171-1182.
WWW Link. 1906
BibRef

An, F.W.[Feng-Wei], Zhang, X.Y.[Xiang-Yu], Luo, A.W.[Ai-Wen], Chen, L.[Lei], Mattausch, H.J.[Hans Jürgen],
A Hardware Architecture for Cell-Based Feature-Extraction and Classification Using Dual-Feature Space,
CirSysVideo(28), No. 10, October 2018, pp. 3086-3098.
IEEE DOI 1811
Feature extraction, Computer architecture, Histograms, Training, Microprocessors, Prototypes, Dual feature, HOG, Haar-like, complementary classifier BibRef

Ustunel, E.[Eser], Masazade, E.[Engin],
Vision-based road slope estimation methods using road lines or local features from instant images,
IET-ITS(13), No. 10, October 2019, pp. 1590-1602.
DOI Link 1909
BibRef

Li, X.[Xiang], Li, J.[Jun], Hu, X.L.[Xiao-Lin], Yang, J.[Jian],
Line-CNN: End-to-End Traffic Line Detection with Line Proposal Unit,
ITS(21), No. 1, January 2020, pp. 248-258.
IEEE DOI 2001
Proposals, Task analysis, Detectors, Feature extraction, Benchmark testing, Real-time systems, Shape, convolutional neural network BibRef

Zhang, G.[Ge], Yan, C.K.[Chao-Kun], Wang, J.L.[Jian-Lin],
Quality-guided lane detection by deeply modeling sophisticated traffic context,
SP:IC(84), 2020, pp. 115811.
Elsevier DOI 2004
Lane detection, Image quality, Convolution neural network BibRef

Cheng, Y.T.[Yi-Ting], Patel, A.[Ankit], Wen, C.[Chenglu], Bullock, D.[Darcy], Habib, A.[Ayman],
Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef
And: A1, A2, A4, A5:
Lidar-based Lane Marking Extraction Through Intensity Thresholding And Deep Learning Approaches: A Pavement-based Assessment,
ISPRS20(B3:507-514).
DOI Link 2012
BibRef

Khan, H.U., Rafaqat Ali, A., Hassan, A., Ali, A., Kazmi, W., Zaheer, A.,
Lane detection using lane boundary marker network with road geometry constraints,
WACV20(1823-1832)
IEEE DOI 2006
Roads, Image segmentation, Feature extraction, Machine learning, Prediction algorithms, Parallel processing, Detectors BibRef

Hu, C., Wang, Z., Qin, Y., Huang, Y., Wang, J., Wang, R.,
Lane Keeping Control of Autonomous Vehicles With Prescribed Performance Considering the Rollover Prevention and Input Saturation,
ITS(21), No. 7, July 2020, pp. 3091-3103.
IEEE DOI 2007
Rollover, Stability analysis, Transient analysis, Tires, Safety, Adaptation models, Autonomous vehicles, lane keeping, rollover prevention BibRef

Zhong, Y.Z.[Yu-Zhong], Zhang, J.W.[Jian-Wei], Li, Y.J.[Ying-Jiang], Geng, T.Y.[Tian-Yu], Wang, M.N.[Mao-Ning],
Robust multi-lane detection method based on semantic discrimination,
IET-ITS(14), No. 9, September 2020, pp. 1142-1152.
DOI Link 2008
BibRef

Ma, Y.[Yang], Zheng, Y.B.[Yu-Bing], Easa, S.[Said], Cheng, J.C.[Jian-Chuan],
Semi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data,
PandRS(167), 2020, pp. 396-417.
Elsevier DOI 2008
Cycling lane, Centerline, Mobile LiDAR, Roadside barrier, Object identification, Methodology BibRef

Qian, Y., Dolan, J.M., Yang, M.,
DLT-Net: Joint Detection of Drivable Areas, Lane Lines, and Traffic Objects,
ITS(21), No. 11, November 2020, pp. 4670-4679.
IEEE DOI 2011
Task analysis, Decoding, Object detection, Semantics, Intelligent vehicles, Roads, Neural networks, Multi-task network, lane line detection BibRef

Xiong, H.[Hui], Yu, D.[Dameng], Liu, J.X.[Jin-Xin], Huang, H.[Heye], Xu, Q.[Qing], Wang, J.Q.[Jian-Qiang], Li, K.Q.A.[Ke-Qi-Ang],
Fast and robust approaches for lane detection using multi-camera fusion in complex scenes,
IET-ITS(14), No. 12, December 2020, pp. 1582-1593.
DOI Link 2011
BibRef

Ravi, R., Cheng, Y.T., Lin, Y.C., Lin, Y.J., Hasheminasab, S.M., Zhou, T., Flatt, J.E., Habib, A.,
Lane Width Estimation in Work Zones Using LiDAR-Based Mobile Mapping Systems,
ITS(21), No. 12, December 2020, pp. 5189-5212.
IEEE DOI 2012
Roads, Laser radar, Feature extraction, Data mining, Surface morphology, Accidents, Lane width estimation, wide lanes BibRef

Wei, Y., Zhang, K., Ji, S.,
Simultaneous Road Surface and Centerline Extraction From Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing,
GeoRS(58), No. 12, December 2020, pp. 8919-8931.
IEEE DOI 2012
Roads, Image segmentation, Remote sensing, Boosting, Feature extraction, Surface topography, Semantics, tracing BibRef

Tang, J.[Jigang], Li, S.[Songbin], Liu, P.[Peng],
A review of lane detection methods based on deep learning,
PR(111), 2021, pp. 107623.
Elsevier DOI 2012
Lane detection, Deep learning, Semantic segmentation, Instance segmentation BibRef

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,
ITS(22), No. 3, March 2021, pp. 1532-1542.
IEEE DOI 2103
Roads, Feature extraction, Semantics, Interference, Training, Image segmentation, Lane line detection, Ripple-GAN BibRef

Zhu, D.[Di], Song, R.[Rui], Chen, H.[Hui], Klette, R.[Reinhard], Xu, Y.Y.[Yan-Yan],
Moment-based multi-lane detection and tracking,
SP:IC(95), 2021, pp. 116230.
Elsevier DOI 2106
ADAS, Multi-lane detection, Multi-lane tracking, Moments, Kalman filter 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 BibRef

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], Wang, X.M.[Xiao-Min],
Multi-scale spatial convolution algorithm for lane line detection and lane offset estimation in complex road conditions,
SP:IC(99), 2021, pp. 116413.
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.L.[Gui-Lin], Zhang, B.P.[Bao-Peng], 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.J.[Yu-Jing], Wang, Z.J.[Ze-Jiang], 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

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

Han, Y.[Yi], Wang, B.[Biyao], Guan, T.[Tian], Tian, D.[Di], Yang, G.F.[Guang-Feng], Wei, W.[Wei], Tang, H.B.[Hong-Bo], Chuah, J.H.[Joon Huang],
Research on Road Environmental Sense Method of Intelligent Vehicle Based on Tracking Check,
ITS(24), No. 1, January 2023, pp. 1261-1275.
IEEE DOI 2301
For rainy day, cloudy day, night and other special scenarios. Roads, Laser radar, Intelligent vehicles, Cameras, Surface emitting lasers, Sorting, Visualization, machine vision BibRef

Pittner, M.[Maximilian], Condurache, A.[Alexandru], Janai, J.[Joel],
3D-SpLineNet: 3D Traffic Line Detection using Parametric Spline Representations,
WACV23(602-611)
IEEE DOI 2302
Measurement, Solid modeling, Shape, Lane detection, Roads, Lighting, Applications: Robotics, 3D computer vision, visual reasoning BibRef

Yang, Q.[Qin], Ma, Y.H.[Ya-Hong], Li, L.S.[Lin-Sen], Su, C.[Chang], Gao, Y.J.[Yu-Jie], Tao, J.X.[Jia-Xin], Huang, Z.T.[Zhen-Tao], Jiang, R.[Rui],
Lightweight lane line detection based on learnable cluster segmentation with self-attention mechanism,
IET-ITS(17), No. 3, 2023, pp. 518-529.
DOI Link 2303
BibRef

Niskanen, I.[Ilpo], Kolli, T.[Tanja], Immonen, M.[Matti], Heikkilä, R.[Rauno], Merisalo, V.[Virve], Tyni, P.[Pekka], Leviäkangas, P.[Pekka],
Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle,
ITS(24), No. 3, March 2023, pp. 3352-3359.
IEEE DOI 2303
Roads, Metals, Snow, Detectors, Meteorology, Magnetic resonance imaging, Ice, Metal detector, road marker, winter BibRef

Shi, P.C.[Pei-Cheng], Zhang, C.H.[Cheng-Hui], Xu, S.C.[Shu-Cai], Qi, H.[Heng], Chen, X.H.[Xin-He],
MT-Net: Fast video instance lane detection based on space time memory and template matching,
JVCIR(91), 2023, pp. 103771.
Elsevier DOI 2303
Lane detection, Jitter, Space-time memory, Template matching, Error propagation BibRef

Song, Y.C.[Yong-Chao], Huang, T.[Tao], Fu, X.[Xin], Jiang, Y.[Yahong], Xu, J.D.[Jin-Dong], Zhao, J.D.[Jin-Dong], Yan, W.Q.[Wei-Qing], Wang, X.[Xuan],
A Novel Lane Line Detection Algorithm for Driverless Geographic Information Perception Using Mixed-Attention Mechanism ResNet and Row Anchor Classification,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Zhou, J.[Jian], Guo, Y.[Yuan], Bian, Y.[Yaoan], Huang, Y.Y.X.[Yuan-Yan-Xian], Li, B.[Bijun],
Lane Information Extraction for High Definition Maps Using Crowdsourced Data,
ITS(24), No. 7, July 2023, pp. 7780-7790.
IEEE DOI 2307
Roads, Data mining, Feature extraction, Information retrieval, Automobiles, Satellites, Lane detection, Autonomous vehicles, intelligent vehicles BibRef

Yu, F.X.[Fu-Xing], Wu, Y.F.[Ya-Feng], Suo, Y.[Yina], Su, Y.[Yaguang],
Shallow Detail and Semantic Segmentation Combined Bilateral Network Model for Lane Detection,
ITS(24), No. 8, August 2023, pp. 8617-8627.
IEEE DOI 2308
Lane detection, Feature extraction, Semantic segmentation, Videos, Task analysis, Convolution, Deep learning, Lane detection, semantic segmentation BibRef

Liu, R.X.[Rui-Xin], Guan, Z.H.[Zhi-Hao], Yuan, Z.[Zejian], Liu, A.[Ao], Zhou, T.[Tong], Kun, T.[Tang], Li, E.[Erlong], Zheng, C.[Chao], Mei, S.Q.[Shu-Qi],
Learning to Detect 3D Lanes by Shape Matching and Embedding,
WACV23(4280-4288)
IEEE DOI 2302
Training, Point cloud compression, Laser radar, Shape, Lane detection, Network topology, Robotics BibRef

Zhang, X.H.[Xiao-Han], Wshah, S.[Safwan],
LanePainter: Lane Marks Enhancement via Generative Adversarial Network,
ICPR22(3668-3675)
IEEE DOI 2212
Lane detection, Roads, Maintenance engineering, Benchmark testing, Generative adversarial networks, Classification algorithms, Safety 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.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

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

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

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

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

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 BibRef

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 Detection,
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 BibRef

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

Parajuli, A.[Avishek], 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 BibRef

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 Detection,
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],
The embeddability of lane detection algorithms on heterogeneous architectures,
ICIP15(4694-4697)
IEEE DOI 1512
ADAS; Embedded Processing; Lane Detection BibRef

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 BibRef

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 clustering and density verification,
MVA15(345-348)
IEEE DOI 1507
Global Positioning System BibRef

Chen, Y.C.[Yu-Chun], Su, T.F.[Te-Feng], Lai, S.H.[Shang-Hong],
Integrated Vehicle and Lane Detection with Distance Estimation,
IMEV14(473-485).
Springer DOI 1504
BibRef

Mazurek, P.[Przemyslaw],
Shallow Convolutional Neural Network and Viterbi Algorithm for Dim Line Tracking,
ICCVG18(375-384).
Springer DOI 1810
BibRef
And:
Directional Filter and the Viterbi Algorithm for Line Following Robots,
ICCVG14(428-435).
Springer DOI 1410
BibRef

Aly, M.,
Real time detection of lane markers in urban streets,
IVS08(7-12).
IEEE DOI BibRef 0800

Truong, Q.B.[Quoc Bao], Lee, B.R.[Byung Ryong], Heo, N.G.[Nam Geon], Yum, Y.J.[Young Jim], Kim, J.G.[Jong Gook],
Lane boundaries detection algorithm using vector lane concept,
ICARCV08(2319-2325).
IEEE DOI 1109
BibRef

Benmansour, N., Labayrade, R., Aubert, D., Glaser, S., Gruyer, D.,
A model driven 3D lane detection system using stereovision,
ICARCV08(1277-1282).
IEEE DOI 1109
BibRef

Liu, X.[Xin], Dai, B.[Bin], Song, J.Z.[Jin-Ze], He, H.[Hangen], Zhang, B.[Bo],
Real-Time Long-Range Lane Detection and Tracking for Intelligent Vehicle,
ICIG11(654-659).
IEEE DOI 1109

See also Visual Saliency Based on Scale-Space Analysis in the Frequency Domain. BibRef

An, X.J.[Xiang-Jing], Li, J.[Jian], Shang, E.[Erke], He, H.[Hangen],
Multi-scale and Multi-orientation Local Feature Extraction for Lane Detection Using High-Level Information,
ICIG11(576-581).
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
BibRef

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
BibRef

Ben Romdhane, N.[Nadra], Hammami, M.[Mohamed], Ben-Abdallah, H.[Hanene],
A Comparative Study of Vision-Based Lane Detection Methods,
ACIVS11(46-57).
Springer DOI 1108

See also Comparative Study of Vision-Based Traffic Signs Recognition Methods, A. BibRef

Wong, D., Deguchi, D.[Daisuke], Ide, I.[Ichiro], Murase, H.[Hiroshi],
Position Interpolation Using Feature Point Scale for Decimeter Visual Localization,
CVRoads15(90-97)
IEEE DOI 1602
Cameras BibRef

Cheng, G., Wang, Y., Qian, Y., Elder, J.H.,
Geometry-Guided Adaptation for Road Segmentation,
CRV20(46-53)
IEEE DOI 2006
Road Surface Segmentation, Domain Adaptation, Transfer Learning BibRef

Cheng, G., Qian, Y., Elder, J.H.[James H.],
Fusing Geometry and Appearance for Road Segmentation,
CVRoads17(166-173)
IEEE DOI 1802
Adaptation models, Computational modeling, Dictionaries, Image segmentation, Meteorology, Roads, Training BibRef

Corral, E.[Eduardo], Elder, J.H.[James H.],
Probabilistic Detection and Grouping of Highway Lane Marks,
CGC10(311).
PDF File. 1006
BibRef

Jiang, Y.[Yan], Gao, F.[Feng], Xu, G.Y.[Guo-Yan],
Computer vision-based multiple-lane detection on straight road and in a curve,
IASP10(114-117).
IEEE DOI 1004
BibRef

Borkar, A.[Amol], Hayes, M.[Monson], Smith, M.T.[Mark T.],
A Template Matching and Ellipse Modeling Approach to Detecting Lane Markers,
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], Aghajan, H.[Hamid],
Self lane assignment using egocentric smart mobile camera for intelligent GPS navigation,
Egocentric09(57-62).
IEEE DOI 0906
BibRef

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 Bag-of-Textons Method,
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],
Road region estimation using a sequence of monocular images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Asai, T.[Toshihiro], Yamaguchi, K.[Koichiro], Kojima, Y.[Yoshiko], Naito, T.[Takashi], Ninomiya, Y.[Yoshiki],
3D Line Reconstruction of a Road Environment Using an In-Vehicle Camera,
ISVC08(II: 897-904).
Springer DOI 0812
BibRef

Lipski, C.[Christian], Scholz, B.[Bjorn], Berger, K.[Kai], Linz, C.[Christian], Stich, T.[Timo], Magnor, M.[Marcus],
A Fast and Robust Approach to Lane Marking Detection and Lane Tracking,
Southwest08(57-60).
IEEE DOI 0803
BibRef

López, A.M.[Antonio M.], Serrat, J.[Joan], Cañero, C.[Cristina], Lumbreras, F.[Felipe],
Robust Lane Lines Detection and Quantitative Assessment,
IbPRIA07(I: 274-281).
Springer DOI 0706
BibRef

Labayrade, R.,
How Autonomous Mapping Can Help a Road Lane Detection System ?,
ICARCV06(1-6).
IEEE DOI 0612
BibRef

Samadzadegan, F., Sarafraz, A., Tabibi, M.,
Automatic lane detection in image sequences for vision based navigation purposes,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Roncella, R., Forlani, G.[Gianfranco],
Automatic lane parameters extraction in mobile mapping sequences,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Dupuis, J., Parizeau, M.,
Evolving a Vision-Based Line-Following Robot Controller,
CRV06(75-75).
IEEE DOI 0607
BibRef

Nourine, R., Boudihir, M.E.[M. Elarbi], Khelifi, S.F.,
Application of Radon Transform to Lane Boundaries Tracking,
ICIAR04(II: 563-571).
Springer DOI 0409
BibRef

Cramer, H., Scheunert, U., Wanielik, G.,
A new approach for tracking lanes by fusing image measurements with map data,
IVS04(607-612).
IEEE DOI 0411
BibRef

Dal Poz, A.P.[Aluir Porfírio], Oliveira da Silva, M.A.[Marco Aurélio],
Active Testing and Edge Analysis for Road Centreline Extraction,
PCV02(B: 44). 0305
BibRef

Toth, C.K.[Charles K.], Grejner-Brzezinska, D.A.[Dorota A.],
Near Real-Time Road Centerline Extraction,
PCV02(A: 362). 0305
BibRef

Muck, K., Nagel, H.H., Middendorf, M.,
Data-Driven Extraction of Curved Intersection Lanemarks from Road Traffic Image Sequences,
ECCV00(II: 411-427).
Springer DOI 0003
BibRef

Behringer, R., Hötzl, S.,
Simultaneous Estimation of Pitch Angle and Lane Width from the Video Image of a Marked Road,
IROS94(xx). BibRef 9400

Kreucher, C., Lakshmanan, S.,
A Frequency Domain Approach to Lane Detection in Roadway Images,
ICIP99(II:31-35).
IEEE DOI BibRef 9900

Descombes, X., Stoica, R., Zerubia, J.,
Two Markov point processes for simulating line networks,
ICIP99(II:36-40).
IEEE DOI Applied to Roads. BibRef 9900

Yu, B., and Jain, A.K.,
Lane Boundary Detection Using a Multiresolution Hough Transform,
ICIP97(II: 748-751).
IEEE DOI BibRef 9700

Inglebert, C.,
Road Line Tracking: An Approach Based On Spatio-Temporal Surface,
ICPR92(I:224-227).
IEEE DOI BibRef 9200

Campbell, N.W., Thomas, B.T.,
Lane Boundary Tracking for an Autonomous Road Vehicle,
BMVC92(xx-yy).
PDF File. 9209
BibRef

Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Lane Changing, Lane-Change, Analysis, Control .