Dynamic integration and online evaluation of vision-based lane detection algorithms
Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is st...
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| Published in: | IET intelligent transport systems Vol. 13; no. 1; pp. 55 - 62 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
01.01.2019
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| ISSN: | 1751-956X, 1751-9578 |
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| Abstract | Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge. In this study, a vision-based lane detection system with dynamic integration and online evaluation is proposed. To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules. First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm. Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence. To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed. By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections. The system works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera. |
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| AbstractList | Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge. In this study, a vision‐based lane detection system with dynamic integration and online evaluation is proposed. To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules. First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm. Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence. To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed. By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections. The system works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera. |
| Author | Velenis, Efstathios Cao, Dongpu Xing, Yang Wang, Huaji Lv, Chen |
| Author_xml | – sequence: 1 givenname: Yang surname: Xing fullname: Xing, Yang organization: 1Advanced Vehicle Engineering Centre, Cranfield University, Cranfield, UK – sequence: 2 givenname: Chen surname: Lv fullname: Lv, Chen organization: 2School of Mechanical and Aerospace Engineering Nanyang Technological University, 639798, Singapore – sequence: 3 givenname: Huaji surname: Wang fullname: Wang, Huaji organization: 3AVL Powertrain UK Ltd, Coventry, CV4 7EZ, UK – sequence: 4 givenname: Dongpu surname: Cao fullname: Cao, Dongpu email: dongpu.cao@uwaterloo.ca organization: 4Department of Mechanical and Mechatronics Engineering, University of Waterloo, N2L 3G1, Canada – sequence: 5 givenname: Efstathios surname: Velenis fullname: Velenis, Efstathios organization: 1Advanced Vehicle Engineering Centre, Cranfield University, Cranfield, UK |
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| Cites_doi | 10.1109/TITS.2013.2246835 10.1145/358669.358692 10.1155/2012/465819 10.1016/0031-3203(81)90009-1 10.1016/j.neunet.2016.12.002 10.1109/VPPC.2015.7352903 10.1109/IVS.2008.4621152 10.1109/TVT.2013.2242910 10.1109/TVT.2013.2281199 10.1177/1687814015593866 10.1109/ITSC.2013.6728473 10.1109/TVT.2016.2582721 10.1109/TMECH.2016.2631897 10.1080/00423114.2014.938663 10.1109/CIMSim.2012.31 10.1109/TITS.2015.2492019 10.1109/TMECH.2016.2533635 10.1109/TITS.2015.2438714 10.1007/s00138-011-0404-2 10.1016/j.imavis.2003.10.003 10.1007/s10514-009-9113-3 10.1109/34.93808 10.1016/j.patcog.2014.10.011 10.1109/TITS.2014.2321108 10.1109/IPTA.2015.7367103 10.1109/TVT.2016.2555853 10.1162/neco.1996.8.1.129 10.1109/TITS.2006.869595 10.1109/ICPR.2014.453 |
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| Keywords | Hough transform algorithm dynamic integration advance driver assistance systems steerable filter false detections integration system object detection vision-based lane detection algorithms SVT system Gaussian mixture model cameras robust lane detection system ego lanes lane missing scenarios road vehicles feature extraction image segmentation edge detection real time low detection confidence traffic engineering computing voting technique online evaluation Hough transforms computer vision Gaussian processes lane sampling lane geometry knowledge primary lane detection module |
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| References | Li, J.; Mei, X.; Prokhorov, D. (C16) 2016 Kim, J.; Jang, G.J.; Kim, J. (C17) 2016; 87 Lv, C.; Zhang, J.; Li, Y. (C4) 2014; 52 Otsu, N. (C32) 1975; 11 Li, Q.; Chen, L.; Li, M. (C23) 2014; 63 de Paula, M.B.; Jung, C.R. (C9) 2015; 16 Nguyen, V.D.; Nguyen, T.T; Nguyen, D.D. (C20) 2013; 62 Huang, A.S.; Moore, D.; Antone, M. (C27) 2009; 26 Cui, D.; Xue, J.; Zheng, N. (C24) 2016; 17 Huang, Y.; Khajepour, A.; Zhu, T. (C3) 2017; 22 Lim, K.H.; Seng, K.P.; Ang, L.-M. (C14) 2012; 2012 Hillel, A.B.; Lerner, R.; Levi, D. (C5) 2014; 25 Xu, L.; Jordan, M.I. (C31) 1996; 8 Ballard, D.H. (C30) 1981; 13 Wang, Y.; Teoh, E.K.; Shen, D. (C13) 2004; 22 Kim, D.; Kim, B.; Chung, T. (C25) 2017; 22 Freeman, W.T.; Adelson, E.H. (C28) 1991; 13 Tan, H.; Zhou, Y.; Zhu, Y. (C12) 2015; 7 Martinez, C.M.; Hu, X.; Dongpu, C. (C2) 2016; 66 Ji, J.; Khajepour, A.; Melek, W.W. (C7) 2017; 66 Sivaraman, S.; Trivedi, M.M. (C19) 2013; 14 Shin, B.-S.; Tao, J.; Klette, R. (C18) 2015; 48 Rose, C.; Britt, J.; Allen, J. (C22) 2014; 15 McCall, J.C.; Trivedi, M.M. (C6) 2006; 7 Fischler, M.A.; Bolles, R.C. (C33) 1981; 24 2004; 22 2015; 16 2012; 2012 2012 1991; 13 2017; 22 2017; 66 2013; 62 2006; 7 2008 1981; 24 2014; 25 1975; 11 2014; 63 2016; 17 2015; 7 2009; 26 2015; 48 2013; 14 18 December 1962 2016; 87 2014; 15 1981; 13 2016 2015 2014 2013 2014; 52 1996; 8 2016; 66 e_1_2_6_32_1 e_1_2_6_10_1 e_1_2_6_31_1 e_1_2_6_30_1 Li J. (e_1_2_6_17_1) 2016 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_14_1 e_1_2_6_11_1 e_1_2_6_34_1 e_1_2_6_12_1 e_1_2_6_18_1 e_1_2_6_15_1 e_1_2_6_16_1 e_1_2_6_21_1 e_1_2_6_20_1 Otsu N. (e_1_2_6_33_1) 1975; 11 e_1_2_6_9_1 e_1_2_6_8_1 e_1_2_6_5_1 e_1_2_6_4_1 e_1_2_6_7_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_24_1 e_1_2_6_3_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_22_1 e_1_2_6_29_1 e_1_2_6_28_1 e_1_2_6_27_1 e_1_2_6_26_1 |
| References_xml | – volume: 13 start-page: 891 issue: 9 year: 1991 end-page: 906 ident: C28 article-title: The design and use of steerable filters publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 22 start-page: 280 issue: 1 year: 2017 end-page: 290 ident: C25 article-title: Lane-level localization using an AVM camera for an automated driving vehicle in urban environments publication-title: IEEE/ASME Trans. Mechatronics – volume: 7 start-page: 20 issue: 1 year: 2006 end-page: 37 ident: C6 article-title: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 26 start-page: 103 issue: 2 year: 2009 end-page: 122 ident: C27 article-title: Finding multiple lanes in urban road networks with vision and LiDAR publication-title: Auton. Robots – volume: 24 start-page: 381 issue: 6 year: 1981 end-page: 395 ident: C33 article-title: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography publication-title: Commun. ACM – volume: 66 start-page: 952 issue: 2 year: 2017 end-page: 964 ident: C7 article-title: Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints publication-title: IEEE Trans. Veh. Technol. – volume: 2012 start-page: 1 year: 2012 end-page: 10 ident: C14 article-title: River flow lane detection and Kalman filtering-based B-spline lane tracking publication-title: Int. J. Veh. Technol. – volume: 13 start-page: 111 issue: 2 year: 1981 end-page: 122 ident: C30 article-title: Generalizing the Hough transform to detect arbitrary shapes publication-title: Pattern Recognit. – year: 2016 ident: C16 article-title: Deep neural network for structural prediction and lane detection in traffic scene publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 52 start-page: 1372 issue: 11 year: 2014 end-page: 1388 ident: C4 article-title: Extended-Kalman-filter-based regenerative and friction blended braking control for electric vehicle equipped with axle motor considering damping and elastic properties of electric powertrain publication-title: Veh. Syst. Dyn. – volume: 16 start-page: 3160 issue: 6 year: 2015 end-page: 3169 ident: C9 article-title: Automatic detection and classification of road lane markings using onboard vehicular cameras publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 66 start-page: 4534 issue: 6 year: 2016 end-page: 4549 ident: C2 article-title: Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective publication-title: IEEE Trans. Veh. Technol. – volume: 87 start-page: 109 year: 2016 end-page: 121 ident: C17 article-title: Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection publication-title: Neural Netw. – volume: 62 start-page: 2453 issue: 6 year: 2013 end-page: 2468 ident: C20 article-title: A fast evolutionary algorithm for real-time vehicle detection publication-title: IEEE Trans. Veh. Technol. – volume: 25 start-page: 727 issue: 3 year: 2014 end-page: 745 ident: C5 article-title: Recent progress in road and lane detection: a survey publication-title: Mach. Vis. Appl. – volume: 14 start-page: 906 issue: 2 year: 2013 end-page: 917 ident: C19 article-title: Integrated lane and vehicle detection, localization, and tracking: a synergistic approach publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 48 start-page: 3333 issue: 11 year: 2015 end-page: 3345 ident: C18 article-title: A superparticle filter for lane detection publication-title: Pattern Recognit. – volume: 63 start-page: 540 issue: 2 year: 2014 end-page: 555 ident: C23 article-title: A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios publication-title: IEEE Trans. Veh. Technol. – volume: 22 start-page: 1037 issue: 2 year: 2017 end-page: 1046 ident: C3 article-title: A supervisory energy-saving controller for a novel anti-idling system of service vehicles publication-title: IEEE/ASME Trans. Mechatronics – volume: 22 start-page: 269 issue: 4 year: 2004 end-page: 280 ident: C13 article-title: Lane detection and tracking using B-snake publication-title: Image Vis. Comput. – volume: 7 start-page: 1 issue: 7 year: 2015 end-page: 12 ident: C12 article-title: Improved river flow and random sample consensus for curve lane detection publication-title: Adv. Mech. Eng. – volume: 8 start-page: 129 issue: 1 year: 1996 end-page: 151 ident: C31 article-title: On convergence properties of the EM algorithm for Gaussian mixtures publication-title: Neural Comput. – volume: 15 start-page: 2615 issue: 6 year: 2014 end-page: 2629 ident: C22 article-title: An integrated vehicle navigation system utilizing lane-detection and lateral position estimation systems in difficult environments for GPS publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 17 start-page: 1039 issue: 4 year: 2016 end-page: 1050 ident: C24 article-title: Real-time global localization of robotic cars in lane level via lane marking detection and shape registration publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 11 start-page: 23 issue: 285–296 year: 1975 end-page: 27 ident: C32 article-title: A threshold selection method from gray-level histograms publication-title: Automatica – volume: 25 start-page: 727 issue: 3 year: 2014 end-page: 745 article-title: Recent progress in road and lane detection: a survey publication-title: Mach. Vis. Appl. – volume: 7 start-page: 20 issue: 1 year: 2006 end-page: 37 article-title: Video‐based lane estimation and tracking for driver assistance: survey, system, and evaluation publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 7 start-page: 1 issue: 7 year: 2015 end-page: 12 article-title: Improved river flow and random sample consensus for curve lane detection publication-title: Adv. Mech. Eng. – volume: 2012 start-page: 1 year: 2012 end-page: 10 article-title: River flow lane detection and Kalman filtering‐based B‐spline lane tracking publication-title: Int. J. Veh. Technol. – volume: 14 start-page: 906 issue: 2 year: 2013 end-page: 917 article-title: Integrated lane and vehicle detection, localization, and tracking: a synergistic approach publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 13 start-page: 111 issue: 2 year: 1981 end-page: 122 article-title: Generalizing the Hough transform to detect arbitrary shapes publication-title: Pattern Recognit. – volume: 48 start-page: 3333 issue: 11 year: 2015 end-page: 3345 article-title: A superparticle filter for lane detection publication-title: Pattern Recognit. – volume: 17 start-page: 1039 issue: 4 year: 2016 end-page: 1050 article-title: Real‐time global localization of robotic cars in lane level via lane marking detection and shape registration publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 8 start-page: 129 issue: 1 year: 1996 end-page: 151 article-title: On convergence properties of the EM algorithm for Gaussian mixtures publication-title: Neural Comput. – volume: 66 start-page: 952 issue: 2 year: 2017 end-page: 964 article-title: Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints publication-title: IEEE Trans. Veh. Technol. – volume: 16 start-page: 3160 issue: 6 year: 2015 end-page: 3169 article-title: Automatic detection and classification of road lane markings using onboard vehicular cameras publication-title: IEEE Trans. Intell. Transp. Syst. – year: 2014 – volume: 22 start-page: 269 issue: 4 year: 2004 end-page: 280 article-title: Lane detection and tracking using B‐snake publication-title: Image Vis. Comput. – year: 2012 – year: 2016 article-title: Deep neural network for structural prediction and lane detection in traffic scene publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 62 start-page: 2453 issue: 6 year: 2013 end-page: 2468 article-title: A fast evolutionary algorithm for real‐time vehicle detection publication-title: IEEE Trans. Veh. Technol. – volume: 26 start-page: 103 issue: 2 year: 2009 end-page: 122 article-title: Finding multiple lanes in urban road networks with vision and LiDAR publication-title: Auton. Robots – volume: 24 start-page: 381 issue: 6 year: 1981 end-page: 395 article-title: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography publication-title: Commun. ACM – volume: 11 start-page: 23 issue: 285–296 year: 1975 end-page: 27 article-title: A threshold selection method from gray‐level histograms publication-title: Automatica – volume: 15 start-page: 2615 issue: 6 year: 2014 end-page: 2629 article-title: An integrated vehicle navigation system utilizing lane‐detection and lateral position estimation systems in difficult environments for GPS publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 52 start-page: 1372 issue: 11 year: 2014 end-page: 1388 article-title: Extended‐Kalman‐filter‐based regenerative and friction blended braking control for electric vehicle equipped with axle motor considering damping and elastic properties of electric powertrain publication-title: Veh. Syst. Dyn. – volume: 22 start-page: 280 issue: 1 year: 2017 end-page: 290 article-title: Lane‐level localization using an AVM camera for an automated driving vehicle in urban environments publication-title: IEEE/ASME Trans. Mechatronics – year: 2008 – volume: 66 start-page: 4534 issue: 6 year: 2016 end-page: 4549 article-title: Energy management in plug‐in hybrid electric vehicles: recent progress and a connected vehicles perspective publication-title: IEEE Trans. Veh. Technol. – volume: 22 start-page: 1037 issue: 2 year: 2017 end-page: 1046 article-title: A supervisory energy‐saving controller for a novel anti‐idling system of service vehicles publication-title: IEEE/ASME Trans. Mechatronics – volume: 13 start-page: 891 issue: 9 year: 1991 end-page: 906 article-title: The design and use of steerable filters publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 87 start-page: 109 year: 2016 end-page: 121 article-title: Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection publication-title: Neural Netw. – year: 2015 – volume: 63 start-page: 540 issue: 2 year: 2014 end-page: 555 article-title: A sensor‐fusion drivable‐region and lane‐detection system for autonomous vehicle navigation in challenging road scenarios publication-title: IEEE Trans. Veh. Technol. – year: 2013 – year: 18 December 1962 – ident: e_1_2_6_20_1 doi: 10.1109/TITS.2013.2246835 – ident: e_1_2_6_34_1 doi: 10.1145/358669.358692 – ident: e_1_2_6_15_1 doi: 10.1155/2012/465819 – ident: e_1_2_6_31_1 doi: 10.1016/0031-3203(81)90009-1 – ident: e_1_2_6_18_1 doi: 10.1016/j.neunet.2016.12.002 – ident: e_1_2_6_11_1 doi: 10.1109/VPPC.2015.7352903 – ident: e_1_2_6_16_1 doi: 10.1109/IVS.2008.4621152 – year: 2016 ident: e_1_2_6_17_1 article-title: Deep neural network for structural prediction and lane detection in traffic scene publication-title: IEEE Trans. Neural Netw. Learn. Syst. – ident: e_1_2_6_21_1 doi: 10.1109/TVT.2013.2242910 – ident: e_1_2_6_24_1 doi: 10.1109/TVT.2013.2281199 – ident: e_1_2_6_13_1 doi: 10.1177/1687814015593866 – ident: e_1_2_6_22_1 doi: 10.1109/ITSC.2013.6728473 – ident: e_1_2_6_2_1 – ident: e_1_2_6_3_1 doi: 10.1109/TVT.2016.2582721 – ident: e_1_2_6_30_1 – ident: e_1_2_6_4_1 doi: 10.1109/TMECH.2016.2631897 – ident: e_1_2_6_5_1 doi: 10.1080/00423114.2014.938663 – volume: 11 start-page: 23 issue: 285 year: 1975 ident: e_1_2_6_33_1 article-title: A threshold selection method from gray‐level histograms publication-title: Automatica – ident: e_1_2_6_12_1 doi: 10.1109/CIMSim.2012.31 – ident: e_1_2_6_25_1 doi: 10.1109/TITS.2015.2492019 – ident: e_1_2_6_26_1 doi: 10.1109/TMECH.2016.2533635 – ident: e_1_2_6_10_1 doi: 10.1109/TITS.2015.2438714 – ident: e_1_2_6_6_1 doi: 10.1007/s00138-011-0404-2 – ident: e_1_2_6_14_1 doi: 10.1016/j.imavis.2003.10.003 – ident: e_1_2_6_28_1 doi: 10.1007/s10514-009-9113-3 – ident: e_1_2_6_29_1 doi: 10.1109/34.93808 – ident: e_1_2_6_19_1 doi: 10.1016/j.patcog.2014.10.011 – ident: e_1_2_6_23_1 doi: 10.1109/TITS.2014.2321108 – ident: e_1_2_6_9_1 doi: 10.1109/IPTA.2015.7367103 – ident: e_1_2_6_8_1 doi: 10.1109/TVT.2016.2555853 – ident: e_1_2_6_32_1 doi: 10.1162/neco.1996.8.1.129 – ident: e_1_2_6_7_1 doi: 10.1109/TITS.2006.869595 – ident: e_1_2_6_27_1 doi: 10.1109/ICPR.2014.453 |
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| Snippet | Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a... |
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| SubjectTerms | advance driver assistance systems cameras computer vision dynamic integration edge detection ego lanes false detections feature extraction Gaussian mixture model Gaussian processes Hough transform algorithm Hough transforms image segmentation integration system lane geometry knowledge lane missing scenarios lane sampling low detection confidence object detection online evaluation primary lane detection module real time road vehicles robust lane detection system Special Issue: Recent Advancements on Electrified, Low Emission and Intelligent Vehicle-Systems steerable filter SVT system traffic engineering computing vision‐based lane detection algorithms voting technique |
| Title | Dynamic integration and online evaluation of vision-based lane detection algorithms |
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