http://iet.metastore.ingenta.com
1887

Dynamic integration and online evaluation of vision-based lane detection algorithms

Dynamic integration and online evaluation of vision-based lane detection algorithms

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

References

    1. 1)
      • 1. National Highway Traffic Safety Administration.: ‘National motor vehicle crash causation survey: report to congress’, National Highway Traffic Safety Administration Technical Report DOT HS 811, 2008o: 059.
    2. 2)
      • 2. Martinez, C.M., Hu, X., Dongpu, C., et al: ‘Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective’, IEEE Trans. Veh. Technol., 2016, 66, (6), pp. 45344549.
    3. 3)
      • 3. Huang, Y., Khajepour, A., Zhu, T., et al: ‘A supervisory energy-saving controller for a novel anti-idling system of service vehicles’, IEEE/ASME Trans. Mechatronics, 2017, 22, (2), pp. 10371046.
    4. 4)
      • 4. Lv, C., Zhang, J., Li, Y.: ‘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’, Veh. Syst. Dyn., 2014, 52, (11), pp. 13721388.
    5. 5)
      • 5. Hillel, A.B., Lerner, R., Levi, D., et al: ‘Recent progress in road and lane detection: a survey’, Mach. Vis. Appl., 2014, 25, (3), pp. 727745.
    6. 6)
      • 6. McCall, J.C., Trivedi, M.M.: ‘Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (1), pp. 2037.
    7. 7)
      • 7. Ji, J., Khajepour, A., Melek, W.W., et al: ‘Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints’, IEEE Trans. Veh. Technol., 2017, 66, (2), pp. 952964.
    8. 8)
      • 8. Suddamalla, U., Kundu, S., Farkade, S., et al: ‘A novel algorithm of lane detection addressing varied scenarios of curved and dashed lanemarks’. 2015 Int. Conf. Image Processing Theory, Tools and Applications (IPTA), Orleans, France, 2015.
    9. 9)
      • 9. de Paula, M.B., Jung, C.R.: ‘Automatic detection and classification of road lane markings using onboard vehicular cameras’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (6), pp. 31603169.
    10. 10)
      • 10. Bounini, F., Gingras, D., Lapointe, V., et al: ‘Autonomous vehicle and real time road lanes detection and tracking’. 2015 IEEE Vehicle Power and Propulsion Conf. (VPPC), Montreal, Canada, 2015.
    11. 11)
      • 11. Ghazali, K., Xiao, R., Ma, J.: ‘Road lane detection using H-maxima and improved Hough transform’. 2012 Fourth Int. Conf. Computational Intelligence, Modelling and Simulation (CIMSiM), Kuantan, Malaysia, 2012.
    12. 12)
      • 12. Tan, H., Zhou, Y., Zhu, Y., et al: ‘Improved river flow and random sample consensus for curve lane detection’, Adv. Mech. Eng., 2015, 7, (7), pp. 112.
    13. 13)
      • 13. Wang, Y., Teoh, E.K., Shen, D.: ‘Lane detection and tracking using B-snake’, Image Vis. Comput., 2004, 22, (4), pp. 269280.
    14. 14)
      • 14. Lim, K.H., Seng, K.P., Ang, L.-M.: ‘River flow lane detection and Kalman filtering-based B-spline lane tracking’, Int. J. Veh. Technol., 2012, 2012, pp. 110.
    15. 15)
      • 15. Aly, M.: ‘Real time detection of lane markers in urban streets’. 2008 IEEE Intelligent Vehicles Symp., Eindhoven, Netherlands, 2008.
    16. 16)
      • 16. Li, J., Mei, X., Prokhorov, D.: ‘Deep neural network for structural prediction and lane detection in traffic scene’, IEEE Trans. Neural Netw. Learn. Syst., 2016.
    17. 17)
      • 17. Kim, J., Jang, G.J., Kim, J., et al: ‘Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection’, Neural Netw., 2016, 87, pp. 109121.
    18. 18)
      • 18. Shin, B.-S., Tao, J., Klette, R.: ‘A superparticle filter for lane detection’, Pattern Recognit., 2015, 48, (11), pp. 33333345.
    19. 19)
      • 19. Sivaraman, S., Trivedi, M.M.: ‘Integrated lane and vehicle detection, localization, and tracking: a synergistic approach’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 906917.
    20. 20)
      • 20. Nguyen, V.D., Nguyen, T.T, Nguyen, D.D., et al: ‘A fast evolutionary algorithm for real-time vehicle detection’, IEEE Trans. Veh. Technol., 2013, 62, (6), pp. 24532468.
    21. 21)
      • 21. Fritsch, J., Kuehnl, T., Geiger, A., et al: ‘A new performance measure and evaluation benchmark for road detection algorithms’. 2013 16th Int. IEEE Conf. IEEE Intelligent Transportation Systems-(ITSC), The Hague, Netherlands, 2013.
    22. 22)
      • 22. Rose, C., Britt, J., Allen, J., et al: ‘An integrated vehicle navigation system utilizing lane-detection and lateral position estimation systems in difficult environments for GPS’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (6), pp. 26152629.
    23. 23)
      • 23. Li, Q., Chen, L., Li, M., et al: ‘A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios’, IEEE Trans. Veh. Technol., 2014, 63, (2), pp. 540555.
    24. 24)
      • 24. Cui, D., Xue, J., Zheng, N.: ‘Real-time global localization of robotic cars in lane level via lane marking detection and shape registration’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (4), pp. 10391050.
    25. 25)
      • 25. Kim, D., Kim, B., Chung, T., et al: ‘Lane-level localization using an AVM camera for an automated driving vehicle in urban environments’, IEEE/ASME Trans. Mechatronics, 2017, 22, (1), pp. 280290.
    26. 26)
      • 26. Satzoda, R.K., Trivedi, M.M.: ‘On performance evaluation metrics for lane estimation’. 2014 22nd Int. Conf. IEEE Pattern Recognition (ICPR), Stockholm, Sweden, 2014.
    27. 27)
      • 27. Huang, A.S., Moore, D., Antone, M., et al: ‘Finding multiple lanes in urban road networks with vision and LiDAR’, Auton. Robots, 2009, 26, (2), pp. 103122.
    28. 28)
      • 28. Freeman, W.T., Adelson, E.H.: ‘The design and use of steerable filters’, IEEE Trans. Pattern Anal. Mach. Intell., 1991, 13, (9), pp. 891906.
    29. 29)
      • 29. Hough Paul, V.C.: ‘Method and means for recognizing complex patterns’, U.S. Patent No. 3,069,654, 18 December 1962.
    30. 30)
      • 30. Ballard, D.H.: ‘Generalizing the Hough transform to detect arbitrary shapes’. Pattern Recognit., 1981, 13, (2), pp. 111122.
    31. 31)
      • 31. Xu, L., Jordan, M.I.: ‘On convergence properties of the EM algorithm for Gaussian mixtures’, Neural Comput., 1996, 8, (1), pp. 129151.
    32. 32)
      • 32. Otsu, N.: ‘A threshold selection method from gray-level histograms’, Automatica, 1975, 11, (285–296), pp. 2327.
    33. 33)
      • 33. Fischler, M.A., Bolles, R.C.: ‘Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography’, Commun. ACM, 1981, 24, (6), pp. 381395.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5256
Loading

Related content

content/journals/10.1049/iet-its.2018.5256
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address