15.3.3.3 Lane Detection, Lane Following, White Line Detection

Chapter Contents (Back)
Lane Following. Lane Detection. Line Following. For driver assistance:
See also Lane Departure Detection, Lane Keeping, Lane Control Assistance, Lateral Control.

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

Li, W., Lu, G.T., Wang, Y.Q.,
Recognizing White Line Markings for Vision-Guided Vehicle Navigation by Fuzzy-Reasoning,
PRL(18), No. 8, August 1997, pp. 771-780. 9801
BibRef

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

Olivieri, M.[Massimiliano], Fabbrizio, V.[Vito], Guerrieri, R.[Roberto], Kramer, A.[Alan],
Method for identifying marking stripes of road lanes,
US_Patent6,212,287, Apr 3, 2001
WWW Link. BibRef 0104

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

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

Toledo-Moreo, R., Zamora-Izquierdo, M.A.,
IMM-Based Lane-Change Prediction in Highways With Low-Cost GPS/INS,
ITS(10), No. 1, March 2009, pp. 180-185.
IEEE DOI 0903
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

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.[Wentao], 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

Kheyrollahi, A.[Alireza], Breckon, T.P.[Toby P.],
Automatic real-time road marking recognition using a feature driven approach,
MVA(23), No. 1, January 2012, pp. 123-133.
WWW Link. 1201
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

Sivaraman, S., Trivedi, M.M.,
Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach,
ITS(14), No. 2, 2013, pp. 906-917.
IEEE DOI 1307
Image edge detection; Kalman filters; driver assistance; lane departure
See also Vehicle Detection by Independent Parts for Urban Driver Assistance. BibRef

Sivaraman, S.[Sayanan], Trivedi, M.M.[Mohan M.],
Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance,
ITS(15), No. 5, October 2014, pp. 2063-2073.
IEEE DOI 1410
data structures BibRef

Satzoda, R.K., Trivedi, M.M.,
Drive Analysis Using Vehicle Dynamics and Vision-Based Lane Semantics,
ITS(16), No. 1, February 2015, pp. 9-18.
IEEE DOI 1502
Computer crashes BibRef

Satzoda, R.K.[Ravi Kumar], Trivedi, M.M.[Mohan M.],
On Enhancing Lane Estimation Using Contextual Cues,
CirSysVideo(25), No. 11, November 2015, pp. 1870-1881.
IEEE DOI 1511
BibRef
Earlier:
On Performance Evaluation Metrics for Lane Estimation,
ICPR14(2625-2630)
IEEE DOI 1412
BibRef
And:
Efficient Lane and Vehicle Detection with Integrated Synergies (ELVIS),
ECVW14(708-713)
IEEE DOI 1409
BibRef
Earlier:
Vision-Based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization,
ECVW13(604-609)
IEEE DOI 1309
Accuracy. embedded system; intelligent driver assistance systems; lane detection. computational efficiency BibRef

Sivaraman, S.[Sayanan], Morris, B.T.[Brendan T.], Trivedi, M.M.[Mohan M.],
Learning multi-lane trajectories using vehicle-based vision,
CVVT11(2070-2076).
IEEE DOI 1201
The other cars. BibRef

McCall, J.C., Trivedi, M.M.,
An integrated, robust approach to lane marking detection and lane tracking,
IVS04(533-537).
IEEE DOI 0411
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

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 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, computer vision, 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

Wen, C.L.[Cheng-Lu], Sun, X.T.[Xiao-Tian], Li, J.[Jonathan], Wang, C.[Cheng], Guo, Y.[Yan], Habib, A.[Ayman],
A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds,
PandRS(147), 2019, pp. 178-192.
Elsevier DOI 1901
Point cloud, Road marking, Extraction, Classification, Completion, Deep learning BibRef

Kurz, F.[Franz], Azimi, S.M.[Seyed Majid], Sheu, C.Y.[Chun-Yu], d'Angelo, P.[Pablo],
Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Wan, R.[Rui], Huang, Y.[Yuchun], Xie, R.[Rongchang], 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., Li, J., Hu, X., Yang, J.,
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.[Chaokun], 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

Lu, C., Hu, F., Cao, D., Gong, J., Xing, Y., Li, Z.,
Transfer Learning for Driver Model Adaptation in Lane-Changing Scenarios Using Manifold Alignment,
ITS(21), No. 8, August 2020, pp. 3281-3293.
IEEE DOI 2008
Adaptation models, Vehicles, Data models, Manifolds, Principal component analysis, Dimensionality reduction, Wheels, local Procrustes analysis 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

Ye, X.Y.[Xing-Yu], Hong, D.S.[Dza-Shiang], Chen, H.H.[Hung-Hao], Hsiao, P.Y.[Pei-Yung], Fu, L.C.[Li-Chen],
A two-stage real-time YOLOv2-based road marking detector with lightweight spatial transformation-invariant classification,
IVC(102), 2020, pp. 103978.
Elsevier DOI 2010
Deep learning, Road marking, Spatial transform, Real-time object detection, Object classification BibRef

Yang, R.H.[Rong-Hao], Li, Q.[Qitao], Tan, J.X.[Jun-Xiang], Li, S.[Shaoda], Chen, X.Y.[Xin-Yu],
Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010
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.[Jianqiang], 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

Qin, Z.[Zequn], Wang, H.[Huanyu], 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