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Recent progress in road and lane detection: a survey

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Abstract

The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.

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References

  1. Thorpe C., Hebert M., Kanade T., Shafer S.: Toward autonomous driving: the CMU Navlab. Part I: perception. IEEE Expert 6, 31–42 (1991)

    Article  Google Scholar 

  2. Huang A.S., Moore D., Antone M., Olson E., Teller S.: Finding multiple lanes in urban road networks with vision and LIDAR. Auton. Robots 26, 103–122 (2009)

    Article  Google Scholar 

  3. McCall J., Trivedi M.: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans. Intell. Transp. Syst. 7, 20–37 (2006)

    Article  Google Scholar 

  4. Labayrade R., Douret J., Laneurit J., Chapuis R.: A reliable and robust lane detection system based on the parallel use of three algorithms for driving safety assistance. IEICE Trans. Inf. Syst. E 89D, 2092–2100 (2006)

    Article  Google Scholar 

  5. Cheng H., Jeng B., Tseng P., Fan K.: Lane detection with moving vehicles in the traffic scenes. IEEE Trans. Intell. Transp. Syst. 7, 571–582 (2006)

    Article  Google Scholar 

  6. Wu S., Chiang H., Perng J., Chen C., Wu B., Lee T.: The heterogeneous systems integration design and implementation for lane keeping on a vehicle. IEEE Trans. Intell. Transp. Syst. 9, 246–263 (2008)

    Article  Google Scholar 

  7. Samadzadegan, F., Sarafraz, A., Tabibi, M.: Automatic lane detection in image sequences for vision based navigation purposes. In: ISPRS Image Engineering and Vision Metrology (2006)

  8. Danescu R., Nedevschi S.: Probabilistic lane tracking in difficult road scenarios using stereovision. IEEE Trans. Intell. Transp. Syst. 10, 272–282 (2009)

    Article  Google Scholar 

  9. Jiang, R., Klette, R., Vaudrey, T., Wang, S.: New lane model and distance transform for lane detection and tracking. In: Computer Analysis of Images and Patterns, pp. 1044–1052 (2009)

  10. Borkar, A., Hayes, M., Smith, M.: Robust lane detection and tracking with RANSAC and Kalman filter. In: International Conference on Image Processing, pp. 3261–3264 (2009)

  11. Shi, X., Kong, B., Zheng, F.: A new lane detection method based on feature pattern. In: International Congress on Image and Signal Processing, pp. 1–5 (2009)

  12. Pomerleau, D.: RALPH: Rapidly adapting lateral position handler. In: IEEE Intelligent Vehicles Symposium (1995)

  13. Zhou T., Xu R., Hu X.F., Ye Q.T.: A lane departure warning system based on virtual lane boundary. J. Inf. Sci. Eng. 24, 293–305 (2008)

    Google Scholar 

  14. Burzio, G., et al.: Investigating the impact of a lane departure warning system in real driving conditions: a subjectivefield operational test. In: European Conference on Human Centred Design for Intelligent Transport Systems (2010)

  15. Barickman, F.S., Smith, L., Jones, R.: Lane departure warning system research and test development. In: NHTSA Paper number 07-0495

  16. Batavia, P.H.: Driver-adaptive lane departure warning systems. CMU-RI-TR-99-25 (1999)

  17. Hofmann U., Rieder A., Dickmanns E.: Radar and vision data fusion for hybrid adaptive cruise control on highways. Mach. Vis. Appl. 14(1), 42–49 (2003)

    Article  Google Scholar 

  18. Gao, T., Aghajan, H.: Self lane assignment using egocentric smart mobile camera for intelligent GPS navigation. In: Workshop on Egocentric Vision, pp. 57–62 (2009)

  19. Jiang, Y., Gao, F., Xu, G.: Computer vision-based multiple-lane detection on straight road and in a curve. In: Image Analysis and Signal Processing, pp. 114–117 (2010)

  20. Lipski, C., Scholz, B., Berger, K., Linz, C., Stich, T., Magnor, M.: A fast and robust approach to lane marking detection and lane tracking. In: Southwest Symposium on Image Analysis and Interpretation, pp. 57–60 (2008)

  21. Kornhauser, A.L., et al.: DARPA Urban Challenge Princeton University Technical Paper (2007). http://www.stanford.edu/~jmayer/papers/darpa07.pdf

  22. Urmson C. et al.: Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot. 25(8), 425–466 (2008)

    Article  Google Scholar 

  23. Bacha A. et al.: Odin: team victor Tango entry in the DARPA urban challenge. J. Field Robot. 25(8), 467–492 (2008)

    Article  Google Scholar 

  24. Montemerlo M. et al.: Junior: the Stanford entry in the urban challenge. J. Field Robot. 25(8), 569–597 (2008)

    Google Scholar 

  25. Rasmussen, C., Korah, T.: On-vehicle and aerial texture analysis for vision-based desert road following. In: CVPR Workshop on Machine Vision for Intelligent Vehicles, vol. III, p. 66 (2005)

  26. Kong, H., Audibert, J., Ponce, J.: Vanishing point detection for road detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 96–103 (2009)

  27. Broggi A., Cattani S.: An agent based evolutionary approach to path detection for off-road vehicle guidance. Pattern Recognit. Lett. 27, 1164–1173 (2006)

    Article  Google Scholar 

  28. Alon, Y., Ferencz, A., Shashua, A.: Off-road path following using region classification and geometric projection constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. I, pp. 689–696 (2006)

  29. Nefian, A., Bradski, G.: Detection of drivable corridors for off-road autonomous navigation. In: International Conference on Image Processing, pp. 3025–3028 (2006)

  30. Katramados, I., Crumpler, S., Breckon, T.: Real-time traversable surface detection by colour space fusion and temporal analysis. In: Computer Vision Systems, pp. 265–274 (2009)

  31. Borkar, A., Hayes, M., Smith, M., Pankanti, S.: A layered approach to robust lane detection at night. In: IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pp. 51–57 (2009)

  32. Kammel, S., Pitzer, B.: LIDAR-based lane marker detection and mapping. In: IEEE Intelligent Vehicles Symposium, pp. 1137–1142 (2008)

  33. US Department of Transportation, Federal Highway Administration, O.o.I.M.: Highway statistics (2005)

  34. von Reyher, A., Joos, A., Winner, H.: A LIDAR-based approach for near range lane detection. In: IEEE Intelligent Vehicles Symposium, pp. 147–152 (2005)

  35. Ogawa, T., Takagi, K.: Lane recognition using on-vehicle LIDAR. In: IEEE Intelligent Vehicles Symposium, pp. 540–545 (2006)

  36. Takagi, K., Morikawa, K., Ogawa, T., Saburi, M.: Road environment recognition using on-vehicle LIDAR. In: IEEE Intelligent Vehicles Symposium, pp. 120–125 (2006)

  37. Hernandez, J., Marcotegui, B.: Filtering of artifacts and pavement segmentation from mobile LIDAR data. In: Laser09, p. 329 (2009)

  38. Cremean, L.B., Murray, R.: Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1661–1666 (2006)

  39. Pradeep, V., Medioni, G., Weiland, J.: Piecewise planar modeling for step detection using stereo vision. In: Computer Vision Applications for the Visually Impaired (2008)

  40. Sach, L., Atsuta, K., Hamamoto, K., Kondo, S.: A robust road profile estimation method for low texture stereo images. In: International Conference on Image Processing, pp. 4273–4276 (2009)

  41. Michael W., Aaron E., Loren K.: Consumer-grade global positioning system (GPS) accuracy and reliability. J. Forestry 103, 169–173 (2005)

    Google Scholar 

  42. Caron, F., Duflos, E., Pomorski, D., Vanheeghe, P.: GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects. Information Fusion, pp. 221–230 (2004)

  43. Yi, Y.: On improving the accuarcy and reliability of GPS/INS based direct sensor georeferencing. Ph.D. dissertation, Ohio State University, Columbus (2007)

  44. Ma, B., Lakshmanan, S., Hero, A.O., I.: Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion. IEEE Trans. Intell. Transp. Syst. 01, 135–147 (2000)

  45. Kaliyaperumal K., Lakshmanan S., Kluge K.: An algorithm for detecting roads and obstacles in radar images. IEEE Trans. Vehicular Technol. 50, 170–182 (2001)

    Article  Google Scholar 

  46. Yamaguchi, K., Watanabe, A., Naito, T., Ninomiya, Y.: Road region estimation using a sequence of monocular images. In: International Conference on Pattern Recognition, pp. 1–4 (2008)

  47. Zhang, G., Zheng, N., Cui, C., Yan, Y., Yuan, Z.: An efficient road detection method in noisy urban environment. In: IEEE Intelligent Vehicles Symposium (2009)

  48. Mobileye homepage. http://www.mobileye.com/manufacturer-products/brochures

  49. Alvarez, J., Lopez, A., Baldrich, R.: Shadow resistant road segmentation from a mobile monocular system. In: Iberian Conference on Pattern Recognition and Image Analysis, II, 9–16 (2007)

  50. Sawano H., Okada M.: A road extraction method by an active contour model with inertia and differential features. IEICE Trans. Inf. Syst. E 89D, 2257–2267 (2006)

    Article  Google Scholar 

  51. Nieto, M., Salgado, L., Jaureguizar, F., Arrospide, J.: Robust multiple lane road modeling based on perspective analysis. In: International Conference on Image Processing, pp. 2396–2399 (2008)

  52. Wang Y., Teoh E., Shen D.: Lane detection and tracking using b-snake. Image Vis. Comput. 22, 269–280 (2004)

    Article  Google Scholar 

  53. Kim Z.: Robust lane detection and tracking in challenging scenarios. IEEE Trans. Intell. Transp. Syst. 9, 16–26 (2008)

    Article  Google Scholar 

  54. Fischler M.A., Bolles R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  55. Tarel, J.P., Ieng, S.S., Charbonnier, P.: Using robust estimation algorithms for tracking explicit curves. In: ECCV ’02: Proceedings of the 7th European Conference on Computer Vision-Part I, pp. 492–507. Springer, London (2002)

  56. Duda R.O., Hart P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)

    Article  Google Scholar 

  57. Liang F., Troung Y., Wong W.: Automatic Bayesian model averaging for linear regression and applications in Bayesian curve fitting. Stat. Sin. 11, 1005–1029 (2001)

    MATH  Google Scholar 

  58. Rissanen, J.: An introduction to the MDL principle (2008). http://www.mdl-research.org/jorma.rissanen/pub/Intro.pdf

  59. Kohler M., Krzyzak A., Schäfer D.: Application of structural risk minimization to multivariate smoothing spline regression estimates. Bernoulli, 8(4), 475–489 (2002)

    MATH  MathSciNet  Google Scholar 

  60. Dean T., Kanazawa K.: A model for reasoning about persistence and causation. Comput. Intell. 5, 142–150 (1989)

    Article  Google Scholar 

  61. Claeskens G., Hjort N.L.: Model Selection and Model Averaging. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  62. Lopez, A., Serrat, J., Canero, C., Lumbreras, F.: Robust lane lines detection and quantitative assessment. In: Iberian Conference on Pattern Recognition and Image Analysis, vol. I, pp. 274–281 (2007)

  63. Dollàr, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: CVPR (2009)

  64. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893. ACM, New York (2005)

  65. Fei-Fei L., Fergus R., Perona P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28, 594–611 (2006)

    Article  Google Scholar 

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Correspondence to Aharon Bar Hillel.

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Bar Hillel, A., Lerner, R., Levi, D. et al. Recent progress in road and lane detection: a survey. Machine Vision and Applications 25, 727–745 (2014). https://doi.org/10.1007/s00138-011-0404-2

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