access icon free 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 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.

Inspec keywords: road vehicles; computer vision; image segmentation; cameras; feature extraction; Gaussian processes; Hough transforms; edge detection; traffic engineering computing; object detection

Other keywords: primary lane detection module; Hough transform algorithm; ego lanes; false detections; integration system; SVT system; vision-based lane detection algorithms; Gaussian mixture model; dynamic integration; advance driver assistance systems; lane geometry knowledge; low detection confidence; steerable filter; lane sampling; real time; robust lane detection system; voting technique; online evaluation; lane missing scenarios

Subjects: Other topics in statistics; Integral transforms; Traffic engineering computing; Integral transforms; Image recognition; Computer vision and image processing techniques; Other topics in statistics

References

    1. 1)
      • 29. Hough Paul, V.C.: ‘Method and means for recognizing complex patterns’, U.S. Patent No. 3,069,654, 18 December 1962.
    2. 2)
      • 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.
    3. 3)
      • 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.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 18. Shin, B.-S., Tao, J., Klette, R.: ‘A superparticle filter for lane detection’, Pattern Recognit., 2015, 48, (11), pp. 33333345.
    10. 10)
      • 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.
    11. 11)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 31. Xu, L., Jordan, M.I.: ‘On convergence properties of the EM algorithm for Gaussian mixtures’, Neural Comput., 1996, 8, (1), pp. 129151.
    15. 15)
      • 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.
    16. 16)
      • 15. Aly, M.: ‘Real time detection of lane markers in urban streets’. 2008 IEEE Intelligent Vehicles Symp., Eindhoven, Netherlands, 2008.
    17. 17)
      • 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.
    18. 18)
      • 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.
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 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.
    23. 23)
      • 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.
    24. 24)
      • 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.
    25. 25)
      • 30. Ballard, D.H.: ‘Generalizing the Hough transform to detect arbitrary shapes’. Pattern Recognit., 1981, 13, (2), pp. 111122.
    26. 26)
      • 13. Wang, Y., Teoh, E.K., Shen, D.: ‘Lane detection and tracking using B-snake’, Image Vis. Comput., 2004, 22, (4), pp. 269280.
    27. 27)
      • 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.
    28. 28)
      • 32. Otsu, N.: ‘A threshold selection method from gray-level histograms’, Automatica, 1975, 11, (285–296), pp. 2327.
    29. 29)
      • 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.
    30. 30)
      • 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.
    31. 31)
      • 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.
    32. 32)
      • 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.
    33. 33)
      • 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.
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