Fig. 1. System overview
Fig. 2. Result of the pre-processing phase: (a) Input image, (b) Result of the Gaussian blur, (c) Result of the Canny Edge, (d) Result of the applied look-up table of the lane thickness.
Fig. 3. Robust filter for detection of the Inner edge: (a) Filter applied to the left lane, (b) Filter applied to the right lane, (c) Specified ROI after select of the ego-lane, (d) Result of applying the proposed filter.
Fig. 4. Select Ego-Lane: (a) Result of the Hough Transform, (b) Set the candidates for the left and right lanes, (c) Method of the Select Ego-Lane, (d) Result of the Select Ego-Lane.
Fig. 5. Calculation of Lateral offset: (a) Measurement of the worldcoordinate for the lane width, (b) Measurement of the number of the pixel for the lane width in the image-coordinate.
Fig. 6. I.MX6Q board
Fig. 7. System architecture
Fig. 8. Experimental result at daytime to the straight lines: (a) A shadowy road, (b) A lot of road markings, (c) A lot of vehicles and weak lane marking.
Fig. 9. Experimental result at night time to the straight lines: (a) Weak lane marking, (b) A lot of road markings, (c) A lot of vehicles.
Fig. 10. Experimental result at daytime to the curve lines: (a) Weak lane marking, (b) Vehicle exist near the curve lanes, (c) Guard rail on the left side of the curve lane.
Fig. 11. Experimental result of fail case: (a) Double yellow left lanes, (b) Weak yellow left lane, (c) White lane with severe damage.
Fig. 12. Lateral offset based departure timing test. (a) White straight lane test using Canny Edge filter, (b) White straight lane test using proposed filter, (c) Yellow straight lane test using proposed filter
Table 1. The result of accuracy of lane detection
Table 2. The result of the accuracy test for lane departure warning
References
- L. Meng, W. Kees, R. V. Der, and Heijden, "Technical feasibility of advanced driver assistance systems (ADAS) for road traffic safety", Transp. Plan. Technol., Vol. 28, No. 3, pp. 167-187, 2005. https://doi.org/10.1080/03081060500120282
- F. Zhang, C. Daniel, and A. Knoll, "Vehicle detection based on lidar and camera fusion", 17th Int. IEEE Conf. on Intell. Transp. Syst., pp. 1620-1625, Qingdao, China, 2014.
- M. A. Turk, D. G. Morgenthaler, K. D. Gremban, and M. Marra, "VITS-A vision system for autonomous land vehicle navigation", IEEE Trans. Pattern Anal. Mach. Intell., Vol. 10, No. 3, pp. 342-361, 1988. https://doi.org/10.1109/34.3899
- M. Bertozzi, A. Broggi, C. Caraffi, M. D. Rose, M. Felisa, and G. Vezzoni, "Pedestrian detection by means of far-infrared stereo vision", Comput. Vis. Image Underst., Vol. 106, No. 2-3, pp. 194-204, 2007. https://doi.org/10.1016/j.cviu.2006.07.016
- J. Son, H. Yoo, S. Kim, and K. Sohn, "Real-time illumination invariant lane detection for lane departure warning system", Expert Syst. Appl., Vol. 42, No. 4, pp. 1816-1824, 2015. https://doi.org/10.1016/j.eswa.2014.10.024
- Y. Huang, Y. Li, X. Hu, and W. Ci, "Lane detection based on inverse perspective transformation and Kalman filter", KSII. Trans. Inter. Inform. Syst., Vol. 12, No. 2, pp. 2739-2744, 2018.
- Y. Ding, Z. Xu, Y. Zhang, and K. Sun, "Fast lane detection based on bird's eye view and improved random sample consensus algorithm", Multimed. Tools Appl., Vol. 76, No. 21, pp. 22979-22998, 2017. https://doi.org/10.1007/s11042-016-4184-6
- Y. Su, Y. Zhang, T. Lu, J. Yang, and H. Kong, "Vanishing point constrained lane detection with a stereo camera", Trans. Intell. Transp. Syst., Vol. 19, No. 8, pp. 2739-2744, 2018. https://doi.org/10.1109/TITS.2017.2751746
- Y. Tian, J. Gelernter, X. Wang, W. Chen, J. Gao, Y. Zhang, and X. Li, "Lane marking detection via deep convolutional neural network", Neurocomputing, Vol. 280, No. 6, pp. 46-55, 2018. https://doi.org/10.1016/j.neucom.2017.09.098
- Q. Lin, Y. J. Han, and H, Hahn, "Real-time lane departure detection based on extended edge-linking algorithm", Second Int. IEEE Conf. on Comput. Res. Dev., pp. 725-730, Kuala Lumpur, Malaysia, 2010.
- S. S. Huang, C. J. Chen, P. Y. Hsiao, and L. C. Fu, "Onboard vision system for lane recognition and front-vehicle detection to enhance driver's awareness", Proc. of IEEE Conf. on Robot. Autom., pp. 2456-2461, New Orleans, LA, 2004.
- C. John, "A computational approach to edge detection", IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, No. 6, pp. 679-698, 1986.
- B. Ristic, S. Arulampalam, and N. Gordon. "Beyond the Kalman filter", IEEE Aerosp. Electron. Syst. Mag., Vol. 19, No. 7, pp. 37-38, 2004.
- C. R. Jung and C. R. Kelber, "A lane departure warning system using lateral offset with uncalibrated camera", Proc. of IEEE Conf. on Intell. Transp. Syst., pp. 102-107, Vienna, Austria, 2005.
- H. Yoo, U. Yang, and K. Sohn, "Gradient-enhancing conversion for illumination-robust lane detection", IEEE Trans. Intell. Transp. Syst., Vol. 14, No. 3, pp. 1083-1094, 2013. https://doi.org/10.1109/TITS.2013.2252427