Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Yang Xing; Chen Lv; Long Chen; Huaji Wang; Hong Wang; Dongpu Cao; Efstathios Velenis; Fei-Yue Wang

doi:10.1109/JAS.2018.7511063

Volume 5 Issue 3

May 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(3): 645-661

Yang Xing, Chen Lv, Long Chen, Huaji Wang, Hong Wang, Dongpu Cao, Efstathios Velenis and Fei-Yue Wang, "Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 645-661, Mar. 2018. doi: 10.1109/JAS.2018.7511063

Citation:

Yang Xing, Chen Lv, Long Chen, Huaji Wang, Hong Wang, Dongpu Cao, Efstathios Velenis and Fei-Yue Wang, "Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 645-661, Mar. 2018. doi: 10.1109/JAS.2018.7511063

Citation:

Yang Xing, Chen Lv, Long Chen, Huaji Wang, Hong Wang, Dongpu Cao, Efstathios Velenis and Fei-Yue Wang, "Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 645-661, Mar. 2018. doi: 10.1109/JAS.2018.7511063

PDF( 4131 KB)

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

doi: 10.1109/JAS.2018.7511063

Yang Xing^1
,,
Chen Lv^2
,,
Long Chen^3
,,
Huaji Wang^2
,,
Hong Wang^4
,,
Dongpu Cao^{4
,
,},
Efstathios Velenis^2
,,
Fei-Yue Wang^5
,

1.
Advanced Vehicle Engineering Centre, Cranfield University, Bedford MK43 0AL, U. K., and also with Vehicle Intelligence Pioneers Ltd, Qingdao, 266000, China
2.
Advanced Vehicle Engineering Centre, Cranfield University, Bedford MK43 0AL, UK
3.
School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510275, China
4.
Mechanical and Mechatronics Engineering with the University of Waterloo, 200 University Avenue West Waterloo, ON, N2L 3G1, Canada
5.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

More Information

Author Bio:
Yang Xing received the B.S. degree in automatic control from Qingdao University of Science and Technology, China, in 2012. He then received the MSc. degree in control systems from the Department of Automatic Control and System Engineering, The University of Sheffield, UK, in 2014. Now he is a Ph. D. candidate for transport systems, Cranfield University, UK. His research interests include driver behavior modelling, driver-vehicle interaction, and advanced driver assistance systems. His work focuses on the understanding of driver behaviors and identification of driver intentions using machine-learning methods for intelligent and automated vehicles.(e-mail: y.xing@cranfield.ac.uk)

Chen Lv (S'14-M'16) is currently a Research Fellow at Advanced Vehicle Engineering Center, Cranfield University, UK. He received the Ph.D. degree at the Department of Automotive Engineering, Tsinghua University, China in 2016. From 2014 to 2015, he was a joint Ph.D. researcher at EECS Dept., University of California, Berkeley. His research focuses on cyber-physical system, hybrid system, advanced vehicle control and intelligence, where he has contributed over 40 papers and obtained 11 patents in China. Dr. Lv serves as a Guest Editor for IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Informatics, and International Journal of Powertrains, and an Associate Editor for International Journal of Electric and Hybrid Vehicles, International Journal of Vehicle Systems Modelling and Testing, International Journal of Science and Engineering for Smart Vehicles, and Journal of Advances in Vehicle Engineering. He received the Highly Commended Paper Award of IMechE UK in 2012, the National Fellowship for Doctoral Student in 2013, the NSK Outstanding Mechanical Engineering Paper Award in 2014, the Tsinghua University Graduate Student Academic Rising Star Nomination Award in 2015, the China SAE Outstanding Paper Award in 2015, the 1st Class Award of China Automotive Industry Scientific and Technological Invention in 2015, and the Tsinghua University Outstanding Doctoral Thesis Award in 2016.(e-mail: c.lyu@cranfield.ac.uk)

Long Chen received the Ph.D. degree from Wuhan University, China, in 2013. He is also a co-trained Ph.D. at Social Robot Lab, the National University of Singapore. He is currently an Associate Professor at the School of Data and Computer Science, Sun Yat-Sen University. His researches focus on intelligent vehicle, including environment perception, scene understanding and 3D reconstruction, where he has contributed more than 40 publications and 10 Chinese patents. Dr. Chen serves as an Associate Editor for IEEE Technical Committee on CyberPhysical Systems Newsletter.(e-mail: chenl46@mail.sysu.edu.cn)

Huaji Wang received the B.S. degree in automotive engineering from Jilin University, China, in 2005, and the Ph.D. degree in Engineering from the University of Cambridge, Cambridge, U.K., in 2016, concentrating on the study of driver/vehicle systems and driver-automation collaboration. He presently works as a Research Fellow in automated driving at Cranfield University, UK.(e-mail: huaji.wang, d.cao@cranfield.ac.uk)

Hong Wang is currently a Postdoc Fellow of Mechanical and Mechatronics Engineering at University of Waterloo, Canada. She received the Ph.D. degree from Beijing Institute of Technology, China in 2015. Her research focuses on the component sizing, modeling of hybrid powertrains and power management control strategies design for hybrid electric vehicles, intelligent control theory and application, autonomous vehicles.(e-mail: hong.wang@uwaterloo.ca)

Dongpu Cao (M'08) received the Ph.D. degree from Concordia University, Canada, in 2008. He is currently an Associate Professor at University of Waterloo, Canada. His research focuses on vehicle control and intelligence, automated driving and parallel driving, where he has contributed more than 130 publications and 1 US patent. He received the ASME AVTT2010 Best Paper Award and 2012 SAE Arch T. Colwell Merit Award. Dr. Cao serves as an Associate Editor for IEEE Transactions on Vehicular Technology, IEEE Transactions on Intelligent Transportation Systems, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, and ASME Journal of Dynamic Systems, Measurement and Control. He has been a Guest Editor for Vehicle System Dynamics, and IEEE Transactions on Human-machine Systems. He has been serving on the SAE International Vehicle Dynamics Standards Committee and a few ASME, SAE, IEEE technical committees.(e-mail: dongpu.cao@uwaterloo.ca)

Efstathios Velenis received the Ph.D. degree from the School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA, in 2006. He is currently a Senior Lecturer with the Advanced Vehicle Engineering Centre, Cranfield University, Cranfield, U.K. His current research interests include vehicle dynamics and control, optimal control for active chassis systems, traction, braking, and handling control for electric/hybrid vehicles, laptime optimization, and tire modeling. He obtained research funding from EPSRC, Innovate U.K., and the European Commission. Dr. Velenis received the Luther Long Award for best Ph.D. dissertation in the area of engineering mechanics at Georgia Tech.(e-mail: e.velenis@cranfield.ac.uk)

Fei-Yue Wang (S'87-M'89-SM'94-F'03) received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Overseas Chinese Talents Program from the State Planning Council and "100Talent Program" from CAS, and in 2002, was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for Research, Education, and Academic Exchanges at the Institute of Automation, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he has been the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang has been elected as Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China and was awarded the Outstanding Scientist by ACM for his research contributions in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009, 2011 and 2015, and IEEE SMC Norbert Wiener Award in 2014.(e-mail: feiyue@ieee.org)
Corresponding author: Dongpu Cao, e-mail: dongpu.cao@uwaterloo.ca
Received Date: 2017-07-10
Accepted Date: 2017-12-06

Abstract

Abstract

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
- Advanced driver assistance systems (ADASs),
- ACP theory,
- benchmark,
- lane detection,
- parallel vision,
- performance evaluation

FullText(HTML)

References(112)

References

[1]	E. Bellis and J. Page, National motor vehicle crash causation survey (NMVCCS), SAS analytical users manual, U. S. Department of Transportation, National Highway Traffic Safety Administration, Washington, DC, USA, Tech. Rep. No. HS-811053, Dec. 2008.
[2]	J. E. Gayko, "Lane departure and lane keeping, " in Handbook of Intelligent Vehicles, A. Eskandarian, Ed. London, UK: Springer, 2012, pp. 689-708.
[3]	C.Visvikis, T.L.Smith, M.Pitcher, and R.Smith, "Study on lane departure warning and lane change assistant systems, " Transport Research Laboratory, Berks, UK, Transport Research Laboratory Project Rep.PPR, 2008, pp.374.
[4]	A. Bar Hillel, R. Lerner, D. Levi, and G. Raz, "Recent progress in road and lane detection: A survey, " Mach. Vis. Appl., vol. 25, no. 3, pp. 727-745, Apr. 2014. doi: 10.1007/s00138-011-0404-2
[5]	J. C. McCall and M. M. Trivedi, "Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation, " IEEE Trans. Intell. Transp. Syst. , vol. 7, no. 1, pp. 20-37, Mar. 2006. http://ieeexplore.ieee.org/document/1603550/
[6]	S.Yenikaya, G.Yenikaya, and E.Duven, "Keeping the vehicle on the road:A survey on on-road lane detection systems, " ACM Comput.Surv.(CSUR), vol.46, no.1, pp.2, 2013.
[7]	J. Fritsch, T. Kuhnl, and A. Geiger, "A new performance measure and evaluation benchmark for road detection algorithms, " in Proc. 16th Int. IEEE Conf. Intelligent Transportation Systems (ITSC 2013), The Hague, Netherlands, 2013, pp. 1693-1700. http://ieeexplore.ieee.org/document/6728473/
[8]	M. Beyeler, F. Mirus, and A. Verl, "Vision-based robust road lane detection in urban environments, " in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Hong Kong, China, 2014, pp. 4920-4925. http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6907580
[9]	D. J. Kang, and M. H. Jung, "Road lane segmentation using dynamic programming for active safety vehicles, " Pattern Recogn. Lett. , vol. 24, no. 16, pp. 3177-3185, Dec. 2003. https://www.sciencedirect.com/science/article/pii/S0167865503001843
[10]	U. Suddamalla, S. Kundu, S. Farkade, and A. Das, "A novel algorithm of lane detection addressing varied scenarios of curved and dashed lanemarks, " in Proc. Int. Conf. Image Processing Theory, Tools and Applications (IPTA), Orleans, France, 2015, pp. 87-92. http://ieeexplore.ieee.org/document/7367103/
[11]	J. M. Collado, C. Hilario, A. de la Escalera, and J. M. Armingol, "Adaptative road lanes detection and classification, " in Proc. 8th Int. Conf. Advanced Concepts for Intelligent Vision Systems, Springer, 2006, pp. 1151-1162. http://dl.acm.org/citation.cfm?id=2092641
[12]	S. Sehestedt, S. Kodagoda, A. Alempijevic, and G. Dissanayake, "Robust lane detection in urban environments, " in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, San Diego, CA, USA, 2007, pp. 123-128. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4399388
[13]	Q. Lin, Y. Han, and H. Hahn, "Real-time lane departure detection based on extended edge-linking algorithm, " in Proc. 2nd Int. Conf. Computer Research and Development, Kuala Lumpur, Malaysia, 2010, pp. 725-730. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5489518
[14]	A. F. Cela, L. M. Bergasa, F. L. Sánchez, and M. A. Herrera, "Lanes detection based on unsupervised and adaptive classifier, " in Proc. 5th Int. Conf. Computational Intelligence, Communication Systems and Networks (CICSyN), Madrid, Spain, 2013, pp. 228-233. http://ieeexplore.ieee.org/document/6571370/
[15]	A. Borkar, M. Hayes, M. T. Smith, and S. Pankanti, "A layered approach to robust lane detection at night, " in Proc. IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, Nashville, TN, USA, 2009, pp. 51-57. http://ieeexplore.ieee.org/abstract/document/4938723/
[16]	C. Kreucher and S. Lakshmanan, "LANA: A lane extraction algorithm that uses frequency domain features, " IEEE Trans. Robot. Autom., vol. 15, no. 2, pp. 343-350, Apr. 1999. http://ieeexplore.ieee.org/document/760356/
[17]	S. Jung, J. Youn, and S. Sull, "Efficient lane detection based on spatiotemporal images, " IEEE Trans. Intell. Transp. Syst., vol. 17, no. 1, pp. 289-295, Jan. 2016. http://ieeexplore.ieee.org/document/7217838/
[18]	J. Xiao, S. T. Li, and B. Sun, "A real-time system for lane detection based on FPGA and DSP, " Sens. Imag. , vol. 17, no. 1, pp. 6, Dec. 2016. doi: 10.1007/s11220-016-0133-8
[19]	U. Ozgunalp and N. Dahnoun, "Lane detection based on improved feature map and efficient region of interest extraction, " in Proc. IEEE Global Conf. Signal and Information Processing (GlobalSIP), Orlando, FL, USA, 2015, pp. 923-927. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7418332
[20]	Y. Wang, D. G. Shen, and E. K. Teoh, "Lane detection using spline model, " Pattern Recogn. Lett. , vol. 21, no. 8, pp. 677-689, Jul. 2000.
[21]	Y. Wang, E. K. Teoh, and D. G. Shen, "Lane detection and tracking using B-Snake, " Image Vis. Comput., vol. 22, no. 4, pp. 269-280, Apr. 2004. https://www.sciencedirect.com/science/article/pii/S0262885603002105
[22]	X. Y. Li, X. Z. Fang, C. Wang, and W. Zhang, "Lane detection and tracking using a parallel-snake approach, " J. Intell. Robot. Syst., vol. 77, no. 3-4, pp. 597-609, Mar. 2015.
[23]	K. H. Lim, K. P. Seng, and L. M. Ang, "River flow lane detection and Kalman filtering-based B-spline lane tracking, " Int. J. Veh. Technol., vol. 2012, pp. 465819, Nov. 2012. https://trid.trb.org/view.aspx?id=1225779
[24]	C. R. Jung and C. R. Kelber, "An improved linear-parabolic model for lane following and curve detection, " in Proc. 18th Brazilian Symp. Computer Graphics and Image Processing (SIBGRAPI'05), Natal, Rio Grande do Norte, Brazil, 2005, pp. 131-138. http://ieeexplore.ieee.org/document/1599093/
[25]	M. Aly, "Real time detection of lane markers in urban streets, " in Proc. IEEE Intelligent Vehicles Symp., Eindhoven, Netherlands, 2008, pp. 7-12.
[26]	A. Borkar, M. Hayes, and M. T. Smith, "Robust lane detection and tracking with ransac and Kalman filter, " in Proc. 16th IEEE Int. Conf. Image Processing (ICIP), Cairo, Egypt, 2009, pp. 3261-3264. http://dl.acm.org/citation.cfm?id=1819631
[27]	A. Lopez, C. Canero, J. Serrat, J. Saludes, F. Lumbreras, and T. Graf, "Detection of lane markings based on ridgeness and RANSAC, " in Proc. IEEE Intelligent Transportation Systems, Vienna, Austria, 2005, pp. 254-259. http://ieeexplore.ieee.org/document/1520139/
[28]	A. Lopez, J. Serrat, C. Cañero, F. Lumbreras, and T. Graf, "Robust lane markings detection and road geometry computation, " Int. J. Automot. Technol., vol. 11, no. 3, pp. 395-407, Jun. 2010.
[29]	Q. Chen and H. Wang, "A real-time lane detection algorithm based on a hyperbola-pair model, " in Proc. IEEE Intelligent Vehicles Symp., Tokyo, Japan, 2006, pp. 510-515. http://ieeexplore.ieee.org/document/1689679/
[30]	H. C. Tan, Y. Zhou, Y. Zhu, D. Y. Yao, and J. Q. Wang, "Improved river flow and random sample consensus for curve lane detection, " Adv. Mech. Eng., vol. 7, no. 7, pp. 1-12, Jul. 2015.
[31]	J. Hur, S. N. Kang, and S. W. Seo, "Multi-lane detection in urban driving environments using conditional random fields, " in Proc. IEEE Intelligent Vehicles Symp. (IV), Gold Coast, QLD, Australia, 2013, 1297-1302. http://ieeexplore.ieee.org/document/6629645/
[32]	F. Bounini, D. Gingras, V. Lapointe, and H. Pollart, "Autonomous vehicle and real time road lanes detection and tracking, " in Proc. IEEE Vehicle Power and Propulsion Conf. (VPPC), Montreal, QC, Canada, 2015, pp. 1-6. http://ieeexplore.ieee.org/document/7352903/
[33]	D. Z. Wu, R. Zhao, and Z. H. Wei, "A multi-segment lane-switch algorithm for efficient real-time lane detection, " in Proc. IEEE Int. Conf. Information and Automation (ICIA), Hailar, China, 2014, pp. 273-278.
[34]	S. Y. Zhou, Y. H. Jiang, J. Q. Xi, J. W. Gong, G. M. Xiong, and H. Y. Chen, "A novel lane detection based on geometrical model and Gabor filter, " in Proc. IEEE Intelligent Vehicles Symposium (IV), San Diego, CA, USA, 2010, pp. 59-64. http://ieeexplore.ieee.org/document/5548087/
[35]	J. W. Niu, J. Lu, M. L. Xu, P. Lv, and X. K. Zhao, "Robust lane detection using two-stage feature extraction with curve fitting, " Pattern Recogn., vol. 59, pp. 225-233, Nov. 2016. https://www.sciencedirect.com/science/article/pii/S0031320315004690
[36]	B. He, R. Ai, Y. Yan, and X. P. Lang, "Lane marking detection based on Convolution Neural Network from point clouds, " in Proc. 19th Int. Conf. Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 2016, pp. 2475-2480.
[37]	J. Li, X. Mei, D. Prokhorov, and D. C. Tao, "Deep neural network for structural prediction and lane detection in traffic scene, " IEEE Trans. Neural Netw. Learn. Syst. , vol. 28, no. 3, pp. 690-703, Mar. 2017. http://ieeexplore.ieee.org/document/7407673/
[38]	A. Gurghian, T. Koduri, S. V. Bailur, K. J. Carey, and V. N. Murali, "DeepLanes: End-to-end lane position estimation using deep neural networks, " in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA, 2016, pp. 38-45.
[39]	X. Li, Q. X. Wu, Y. Kou, L. Hou, and H. Yang, "Lane detection based on spiking neural network and Hough transform, " in Proc. 8th Int. Congr. Image and Signal Processing (CISP), Shenyang, China, 2015, pp. 626-630. http://ieeexplore.ieee.org/document/7407954/
[40]	J. Kim, J. Kim, G. J. Jang, and M. Lee, "Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection, " Neural Netw. , vol. 87, pp. 109-121, Mar. 2017.
[41]	B. He, R. Ai, Y. Yan, and X. P. Lang, "Accurate and robust lane detection based on Dual-View Convolutional Neutral Network, " in Proc. IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 2016, pp. 1041-1046.
[42]	M. Revilloud, D. Gruyer, and M. C. Rahal, "A new multi-agent approach for lane detection and tracking, " in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Stockholm, Sweden, 2016, pp. 3147-3153. http://ieeexplore.ieee.org/document/7487482/
[43]	M. Bertozzi, A. Broggi, A. Fascioli, and A. Tibaldi, "An evolutionary approach to lane markings detection in road environments, " Atti del, vol. 6, pp. 627-636, Aug. 2002.
[44]	L. W. Tsai, J. W. Hsieh, C. H. Chuang, and K. C. Fan, "Lane detection using directional random walks, " in Proc. IEEE Intelligent Vehicles Symp. , Eindhoven, Netherlands, 2008, pp. 303-306. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4621271
[45]	L. Bai and Y. Wang, "Road tracking using particle filters with partition sampling and auxiliary variables, " Comput. Vis. Image Understand. , vol. 115, no. 10, pp. 1463-1471, Oct. 2011. https://www.sciencedirect.com/science/article/pii/S1077314211001421
[46]	R. Danescu and S. Nedevschi, "Probabilistic lane tracking in difficult road scenarios using stereovision, " IEEE Trans. Intell. Transp. Syst. , vol. 10, no. 2, pp. 272-282, Jun. 2009. http://ieeexplore.ieee.org/document/4895220/
[47]	Z. Kim, "Robust lane detection and tracking in challenging scenarios, " IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 16-26, Mar. 2008.
[48]	B. S. Shin, J. L. Tao, and R. Klette, "A superparticle filter for lane detection, " Pattern Recogn. , vol. 48, no. 11, pp. 3333-3345, Nov. 2015.
[49]	A. Das, S. S. Murthy, and U. Suddamalla, "Enhanced algorithm of automated ground truth generation and validation for lane detection system by M²BMT, " IEEE Trans. Intell. Transp. Syst., vol. 18, no. 4, pp. 996-1005, Apr. 2017. http://ieeexplore.ieee.org/document/7536635/
[50]	R. Labayrade, S. S. Leng, and D. Aubert, "A reliable road lane detector approach combining two vision-based algorithms, " in Proc. 7th Int. IEEE Conf. Intelligent Transportation Systems, Washington, WA, USA, 2004, pp. 149-154. http://ieeexplore.ieee.org/iel5/9625/30418/01398888.pdf?arnumber=1398888
[51]	R. Labayrade, J. Douret, J. Laneurit, and R. Chapuis, "A reliable and robust lane detection system based on the parallel use of three algorithms for driving safety assistance, " IEICE Trans. Inform. Syst. , vol. E89-D, no. 7, pp. 2092-2100, Jul. 2006. https://trid.trb.org/view/842404
[52]	D. C. Hernández, D. Seo, and K. H. Jo, "Robust lane marking detection based on multi-feature fusion, " in Proc. 9th Int. Conf. Human System Interactions (HSI), Portsmouth, UK, 2016, pp. 423-428.
[53]	Y. U. Yim and S. Y. Oh, "Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving, " IEEE Trans. Intell. Transp. Syst. , vol. 4, no. 4, pp. 219-225, Dec. 2003. http://ieeexplore.ieee.org/document/1260588/
[54]	M. Felisa and P. Zani, "Robust monocular lane detection in urban environments, " in Proc. IEEE Intelligent Vehicles Symposium (IV), San Diego, CA, USA, 2010, pp. 591-596.
[55]	M. Bertozzi and A. Broggi, "GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection, " IEEE Trans. Image Process., vol. 7, no. 1, pp. 62-81, Jan. 1998. http://ieeexplore.ieee.org/document/650851/
[56]	A. S. Huang, D. Moore, M. Antone, E. Olson, and S. Teller, "Finding multiple lanes in urban road networks with vision and lidar, " Auton. Robots, vol. 26, no. 2-3, pp. 103-122, Apr. 2009. doi: 10.1007/s10514-009-9113-3
[57]	H. Y. Cheng, B. S. Jeng, P. T. Tseng, and K. C. Fan, "Lane detection with moving vehicles in the traffic scenes, " IEEE Trans. Intell. Transp. Syst. , vol. 7, no. 4, pp. 571-582, Dec. 2006.
[58]	S. Sayanan and M. M. Trivedi, "Integrated lane and vehicle detection, localization, and tracking: A synergistic approach, " IEEE Trans. Intell. Transp. Syst. , vol. 14, no. 2, pp. 906-917, Jun. 2013. http://ieeexplore.ieee.org/document/6475185/
[59]	C. F. Wu, C. J. Lin, and C. Y. Lee, "Applying a functional neurofuzzy network to real-time lane detection and front-vehicle distance measurement, " IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev. ), vol. 42, no. 4, pp. 577-589, Jul. 2012.
[60]	S. S. Huang, C. J. Chen, P. Y. Hsiao, and L. C. Fu, "On-board vision system for lane recognition and front-vehicle detection to enhance driver's awareness, " in Proc. IEEE Int. Conf. Robotics and Automation, New Orleans, LA, USA, vol. 3, pp. 2456-2461, Jul. 2004. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1307429
[61]	R. K. Satzoda and M. M. Trivedi, "Efficient lane and vehicle detection with integrated synergies (ELVIS), " in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, OH, USA, 2014, pp. 708-713.
[62]	H. Kim, Y. Lee, T. Woo, and H. Kim, "Integration of vehicle and lane detection for forward collision warning system, " in Proc. 6th Int. Conf. Consumer Electronics-Berlin (ICCE-Berlin), Berlin, Germany, 2016, 5-8. http://ieeexplore.ieee.org/document/7684703/
[63]	B. Qin, W. Liu, X. Shen, Z. J. Chong, T. Bandyopadhyay, M. H. Ang, E. Frazzoli, and D. Rus, "A general framework for road marking detection and analysis, " in Proc. 16th Int. IEEE Conf. Intelligent Transportation Systems-(ITSC), The Hague, Netherlands, 2013, pp. 619-625.
[64]	A. Kheyrollahi and T. P. Breckon, "Automatic real-time road marking recognition using a feature driven approach, " Mach. Vis. Appl., vol. 23, no. 1, pp. 123-133, Jan. 2012. doi: 10.1007/s00138-010-0289-5
[65]	J. Greenhalgh, and M. Mirmehdi, "Detection and recognition of painted road surface markings, " in Proc. 4th Int. Conf. Pattern Recognition Applications and Methods, Lisbon, Portugal, 2015, pp. 130-138.
[66]	G. L. Oliveira, W. Burgard, and T. Brox, "Efficient deep models for monocular road segmentation, " in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016, pp. 4885-4891. http://ieeexplore.ieee.org/document/7759717/
[67]	H. Kong, J. Y. Audibert, and J. Ponce, "Vanishing point detection for road detection, " in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 96-103. http://ieeexplore.ieee.org/document/5206787/
[68]	L. Dan, "StixelNet: A deep convolutional network for obstacle detection and road segmentation, " in Proc. 26th British Machine Vision Conference, Swansea, UK, 2015. http://www.bmva.org/bmvc/2015/papers/paper109/
[69]	G. P. Stein, Y. Gdalyahu, and A. Shashua, "Stereo-assist: Top-down stereo for driver assistance systems, " in Proc. IEEE Intelligent Vehicles Symp. (IV), San Diego, CA, USA, 2010, pp. 723-730. http://ieeexplore.ieee.org/document/5548019/
[70]	E. Raphael and R. Kiefer, "Development of a camera-based forward collision alert system, " SAE Int. J. Passeng. Cars-Mech. Syst. , vol. 4, no. 1, pp. 467-478, Dec. 2011.
[71]	B. Ma, S. Lakahmanan, and A. Hero, "Road and lane edge detection with multisensor fusion methods, " in Proc. Int. Conf. Image Processing, Kobe, Japan, vol. 2, pp. 686-690, Oct. 1999. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=822983
[72]	M. Beyeler, F. Mirus, and A. Verl, "Vision-based robust road lane detection in urban environments, " in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Hong Kong, China, 2014, pp. 4920-4925. http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6907580
[73]	U. Ozgunalp, R. Fan, X. Ai, and N. Dahnoun, "Multiple lane detection algorithm based on novel dense vanishing point estimation, " IEEE Trans. Intell. Transp. Syst. , vol. 18, no. 3, pp. 621-632, Mar. 2017. http://ieeexplore.ieee.org/document/7534770/
[74]	C. Lipski, B. Scholz, K. Berger, C. Linz, T. Stich, and M. Magnor, "A fast and robust approach to lane marking detection and lane tracking, " in Proc. IEEE Southwest Symp. Image Analysis and Interpretation, Santa Fe, NM, USA, 2008, pp. 57-60.
[75]	D. Kim, B. Kim, T. Chung, and K. Yi, "Lane-level localization using an AVM camera for an automated driving vehicle in urban environments, " IEEE/ASME Trans. Mechatron. , vol. 22, no. 1, pp. 280-290, Feb. 2017. http://ieeexplore.ieee.org/document/7433431/
[76]	H. G. Jung, Y. H. Lee, H. J. Kang, and J. Kim, "Sensor fusion-based lane detection for LKS+ACC system, " Int. J. Automot. Technol. , vol. 10, no. 2, pp. 219-228, Apr. 2009.
[77]	D. X. Cui, J. R. Xue, and N. N. Zheng, "Real-time global localization of robotic cars in lane level via lane marking detection and shape registration, " IEEE Trans. Intell. Transp. Syst., vol. 17, no. 4, pp. 1039-1050, Apr. 2016. http://ieeexplore.ieee.org/document/7322256/
[78]	Y. Jiang, F. Gao, and G. Y. Xu, "Computer vision-based multiple-lane detection on straight road and in a curve, " in Proc. Int. Conf. Image Analysis and Signal Processing (IASP), Zhejiang, China, 2010, pp. 114-117.
[79]	C. Rose, J. Britt, J. Allen, and D. Bevly, "An integrated vehicle navigation system utilizing lane-detection and lateral position estimation systems in difficult environments for GPS, " IEEE Trans. Intell. Transp. Syst. , vol. 15, no. 6, pp. 2615-2629, Dec. 2014. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6822610
[80]	Q. Q. Li, L. Chen, M. Li, S. L. Shaw, and A. Nuchter, "A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios, " IEEE Trans. Veh. Technol. , vol. 63, no. 2, pp. 540-555, Feb. 2014. http://ieeexplore.ieee.org/document/6594920/
[81]	S. Kammel and B. Pitzer, "Lidar-based lane marker detection and mapping, "in Proc. IEEE Intelligent Vehicles Symp. , Eindhoven, Netherlands, 2008, pp. 1137-1142.
[82]	M. Manz, M. Himmelsbach, T. Luettel, and H. J. Wuensche, "Detection and tracking of road networks in rural terrain by fusing vision and LIDAR, "in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), San Francisco, CA, USA, 2011, pp. 4562-4568.
[83]	M. Schreiber, C. Knoppel, and U. Franke, "Laneloc: Lane marking based localization using highly accurate maps, "in Proc. IEEE Intelligent Vehicles Symp. (IV), Gold Coast, QLD, Australia, 2013, pp. 449-454. http://ieeexplore.ieee.org/document/6629509/
[84]	J. M. Clanton, D. M. Bevly, and A. S. Hodel, "A low-cost solution for an integrated multisensor lane departure warning system, "IEEE Trans. Intell. Transp. Syst. , vol. 10, no. 1, pp. 47-59, Mar. 2009.
[85]	M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Hoffmann, B. Huhnke, D. Johnston, S. Klumpp, D. Langer, A. Levandowski, J. Levinson, J. Marcil, D. Orenstein, J. Paefgen, I. Penny, A. Petrovskaya, M. Pflueger, G. Stanek, D. Stavens, A. Vogt, and S. Thrun, "Junior: The stanford entry in the urban challenge, "J. Field Robot. , vol. 25, no. 9, pp. 569-597, Sep. 2008.
[86]	M.Buehler, K.Iagnemma, and S.Singh, The DARPA Urban Challenge:Autonomous Vehicles in City Traffic.Berlin Heidelberg, Germany:Springer, 2009.
[87]	P. Lindner, E. Richter, G. Wanielik, K. Takagi, and A. Isogai, "Multichannel lidar processing for lane detection and estimation, " in Proc. 12th Int. IEEE Conf. Intelligent Transportation Systems, St. Louis, MO, USA, 2009, pp. 1-6.
[88]	S. Shin, I. Shim, and I. S. Kweon, "Combinatorial approach for lane detection using image and LIDAR reflectance, "in Proc. 12th Int. Conf. Ubiquitous Robots and Ambient Intelligence (URAI), Goyang, South Korea, 2015, pp. 485-487.
[89]	P. Amaradi, N. Sriramoju, L. Dang, G. S. Tewolde, and J. Kwon, "Lane following and obstacle detection techniques in autonomous driving vehicles, "in Proc. IEEE Int. Conf. Electro Information Technology (EIT), Grand Forks, ND, USA, 2016, pp. 674-679.
[90]	D. Klaus, "Roadway detection and lane detection using multilayer laserscanner, "in Advanced Microsystems for Automotive Applications 2005, J. Valldorf, and W. Gessner, Eds. Berlin Heidelberg, Germany: Springer, 2005, pp. 197-213. doi: 10.1007/3-540-27463-4_15
[91]	D. C. Hernández, V. D. Hoang, and K. H. Jo, "Lane surface identification based on reflectance using laser range finder, "in Proc. IEEE/SICE Int. Symp. System Integration (SII), Tokyo, Japan, 2014, pp. 621-625.
[92]	J. Sparbert, K. Dietmayer, and D. Streller, "Lane detection and street type classification using laser range images, "in Proc. IEEE Intelligent Transportation Systems, Oakland, CA, USA, 2001, pp. 454-459.
[93]	A. Broggi, S. Cattani, P. P. Porta, and P. Zani, "A laserscanner-vision fusion system implemented on the terramax autonomous vehicle, "in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Beijing, China, 2006, pp. 111-116.
[94]	H. J. Zhao, M. Chiba, R. Shibasaki, X. W. Shao, J. S. Cui, and H. B. Zha, "A laser-scanner-based approach toward driving safety and traffic data collection, "IEEE Trans. Intell. Transp. Syst. , vol. 10, no. 3, pp. 534-546, Sep. 2009. http://ieeexplore.ieee.org/document/5164971/
[95]	A. Borkar, M. Hayes, and M. T. Smith, "A novel lane detection system with efficient ground truth generation, "IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 365-374, Mar. 2012.
[96]	C. W. Lin, H. Y. Wang, and D. C. Tseng, "A robust lane detection and verification method for intelligent vehicles, "in Proc. 3rd Int. Symp. Intelligent Information Technology Application, Shanghai, China, vol. 1, pp. 521-524, Nov. 2009.
[97]	J. H. Yoo, S. W. Lee, S. K. Park, and D. H. Kim, "A robust lane detection method based on vanishing point estimation using the relevance of line segments, "IEEE Trans. Intell. Transp. Syst., vol. 18, no. 12, pp. 3254-3266, Dec. 2017. http://ieeexplore.ieee.org/document/7888516/
[98]	L. Li, W. L. Huang, Y. H. Liu, N. N. Zheng, and F. Y. Wang, Intelligence testing for autonomous vehicles: A new approach, "IEEE Trans. Intell. Veh. , vol. 1, no. 2, pp. 158-166, Jun. 2016.
[99]	K. C. Kluge, "Performance evaluation of vision-based lane sensing: Some preliminary tools, metrics, and results, "in Proc. Conf. Intelligent Transportation Systems, Boston, MA, USA, 1997, pp. 723-728.
[100]	T. Veit, J. P. Tarel, P. Nicolle, and P. Charbonnier, "Evaluation of road marking feature extraction, "in Proc. 11th Int. IEEE Conf. Intelligent Transportation Systems, Beijing, China, 2008, pp. 174-181. http://ieeexplore.ieee.org/document/4732564/
[101]	J. C. McCall and M. M. Trivedi, "Performance evaluation of a vision based lane tracker designed for driver assistance systems, "in Proc. Intelligent Vehicles Symp., Las Vegas, NV, USA, 2005, pp. 153-158.
[102]	R. K. Satzoda and M. M. Trivedi, "On performance evaluation metrics for lane estimation, "in Proc. 22nd Int. Conf. Pattern Recognition, Stockholm, Sweden, 2014, pp. 2625-2630.
[103]	C. R. Jung and C. R. Kelber, "A robust linear-parabolic model for lane following, "in Proc. 17th Brazilian Symp. Computer Graphics and Image Processing, Curitiba, Brazil, 2004, pp. 72-79.
[104]	M. Haloi and D. B. Jayagopi, "A robust lane detection and departure warning system, "in Proc. IEEE Intelligent Vehicles Symp. , Seoul, South Korea, 2015, pp. 126-131. http://ieeexplore.ieee.org/document/7225674/
[105]	F. Y. Wang, "Parallel system methods for management and control of complex systems, "Control Dec. , vol. 19, no. 5, pp. 485-489, 514, May 2004.
[106]	F. Y. Wang, "Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, " IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630-638, Sep. 2010.
[107]	F. Y. Wang, "Artificial societies, computational experiments, and parallel systems: A discussion on computational theory of complex socialeconomic systems, " Complex Syst. Complexity Sci., vol. 1, no. 4, pp. 25-35, Oct. 2004. http://en.cnki.com.cn/Article_en/CJFDTOTAL-FZXT200404001.htm
[108]	L. Li, Y. L. Lin, D. P. Cao, N. N. Zheng, and F. Y. Wang, "Parallel learning-a new framework for machine learning, "Acta Autom. Sinica, vol. 43, no. 1, pp. 1-8, Jan. 2017.
[109]	K. F. Wang, C. Gou, N. N. Zheng, J. M. Rehg, and F. Y. Wang, Parallel vision for perception and understanding of complex scenes: Methods, framework, and perspectives, "Artif. Intell. Rev., vol. 48, no. 3, pp. 299-329, Oct. 2017. doi: 10.1007/s10462-017-9569-z
[110]	F. Y. Wang, N. N. Zheng, D. P. Cao, C. M. Martinez, L. Li, and T. Liu, Parallel driving in CPSS: A unified approach for transport automation and vehicle intelligence, "IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 577-587, Sep. 2017.
[111]	C. Lv, Y. H. Liu, X. S. Hu, H. Y. Guo, D. P. Cao, and F. Y. Wang, Simultaneous observation of hybrid states for cyber-physical systems: A case study of electric vehicle powertrain, "IEEE Trans. Cybern., 2018, to be published, doi: 10.1109/TCYB.2017.2738003.
[112]	D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, "Mastering the game of Go with deep neural networks and tree search, "Nature, vol. 529, no. 7587, pp. 484-489, Jan. 2016.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(4) / Tables(2)

Get Citation

PDF

XML

Article Metrics

Article views (2838) PDF downloads(437)

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

doi: 10.1109/JAS.2018.7511063

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content