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Implementation of autonomous driving of a ground vehicle for narrow high-curvature roads using surround view images

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Abstract

In this paper, a new approach for the implementation of autonomous driving of a ground vehicle on narrow and high-curvature roads using a surround view system is proposed. The approach called the independent frame consecutive searching technique is an algorithm which is developed to detect the left and right lane markers of a lane stably and to generate virtual lane marker lines especially when the roads are narrow and have sudden corners and windings with some faded lane markers. Then the steering angle of an autonomous vehicle is calculated so that the vehicle drives along the desired path after deriving the smoothed virtual centerline. In the real-time experiment, the suggested approach achieved robust detection of lane markers using surround view images and drove a test vehicle autonomously with the generated desired paths on such narrow and high-curvature roads with any lane departures. Therefore, this study shows that the method can be applied to the development of autonomous vehicles.

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References

  1. Álvarez JM, López AM, Baldrich R (2007) Shadow resistant road segmentation from a mobile monocular system. In: Iberian Conference on Pattern Recognition and Image Analysis, 2007. Springer, pp 9–16. doi:https://doi.org/10.1007/978-3-540-72849-8_2

  2. Aly M (2008) Real time detection of lane markers in urban streets. In: Intelligent Vehicles Symposium, 2008 IEEE, 2008. IEEE, pp 7–12. doi:https://doi.org/10.1109/IVS.2008.6421152

  3. Bertozzi M, Broggi A (1998) GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans Image Process 7(1):62–81. https://doi.org/10.1109/83.650851

    Article  Google Scholar 

  4. Boggavarapu LNP, Vaddi RS, Anne KR et al (2011) A robust multi color lane marking detection approach for Indian scenario. Int J Adv Comput Sci Appl 2(5):18–26. https://doi.org/10.14569/IJACSA.2011.020512

    Article  Google Scholar 

  5. Chen Y, Li L (2014) Advances in intelligent vehicles. Academic Press

  6. Cheng HY, Jeng BS, Tseng PT et al (2006) Lane detection with moving vehicles in the traffic scenes. IEEE Trans Intell Transp Syst 7(4):571–582. https://doi.org/10.1109/TITS.2006.883940

    Article  Google Scholar 

  7. Hillel AB, Lerner R, Levi D et al (2014) Recent progress in road and lane detection: a survey. Mach Vis Appl 25(3):727–745. https://doi.org/10.1007/s00138-011-0404-2

    Article  Google Scholar 

  8. Huang J, Kong B, Li B et al (2007) A new method of unstructured road detection based on HSV color space and road features. In: Information Acquisition, 2007. ICIA'07. International Conference on, 2007. IEEE, pp 596–601. doi:https://doi.org/10.1109/ICIA.2007.4295802

  9. Hughes C, Denny P, Glavin M et al (2010) Equidistant fish-eye calibration and rectification by vanishing point extraction. IEEE Trans Pattern Anal Mach Intell 32(12):2289–2296. https://doi.org/10.1109/TPAMI.2010.159

    Article  Google Scholar 

  10. Jazar RN (2017). Vehicle dynamics: theory and application. Springer

  11. Kaneko T, Iizuka H, Kageyama I (2006) Steering control for advanced guideway bus system with all-wheel steering system. Veh Syst Dyn 44(sup1):741–746. https://doi.org/10.1080/00423110600885731

    Article  Google Scholar 

  12. Kang DJ, Choi JW, Kweon IS (1996) Finding and tracking road lanes using" line-snakes". In: Intelligent Vehicles Symposium, 1996., Proceedings of the 1996 IEEE, 1996. IEEE, pp 189–194. doi:https://doi.org/10.1109/IVS.1996.566376

  13. Katramados I, Crumpler S, Breckon TP (2009) Real-time traversable surface detection by colour space fusion and temporal analysis. In: International Conference on Computer Vision Systems, 2009. Springer, pp 265–274. doi:https://doi.org/10.1007/978-3-642-04667-4_27

  14. Kiss D, Tevesz G (2017) Autonomous path planning for road vehicles in narrow environments: an efficient continuous curvature approach. J Adv Transp 2017:27. https://doi.org/10.1155/2017/2521638

    Article  Google Scholar 

  15. Koopman P, Wagner M (2017) Autonomous vehicle safety: an interdisciplinary challenge. IEEE Intell Transp Syst Mag 9(1):90–96. https://doi.org/10.1109/MITS.2016.2583491

    Article  Google Scholar 

  16. Kum CH, Cho DC, Ra MS et al (2013) Lane detection system with around view monitoring for intelligent vehicle. In: International SoC Design Conference (ISOCC), Busan, 2013, pp 215–218. doi: https://doi.org/10.1109/ISOCC.2013.6864011

  17. Lan X, Di Cairano S (2018) Continuous curvature path planning for semi-autonomous vehicle maneuvers using RRT. In: Control Conference (ECC), 2015 European, 2015. IEEE, pp 2360–2365. doi:https://doi.org/10.1109/ECC.2015.7330891

  18. Lee HS, Kim SW, Park SY et al (2017) AVM/LiDAR sensor based lane marking detection method for automated driving on complex urban roads. In: Intelligent Vehicles Symposium (IV), 2017 IEEE, Los Angeles, 2017. IEEE, pp 1434–1439. doi: https://doi.org/10.1109/IVS.2017.7995911

  19. Li S, Oshima H, Nakanishi I et al (2011) Lane departure estimation by side fisheye camera. In: Mechatronics and Automation (ICMA), 2011 International Conference on, 2011. IEEE, pp 1004–1009. doi: https://doi.org/10.1109/ICMA.2011.5985797

  20. Liu Y, Zhang D, Lu G, Ma W-Y (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282. https://doi.org/10.1016/j.patcog.2006.04.045

    Article  MATH  Google Scholar 

  21. Rossle S, Kriger W, Gengenbach V (1993) Real-time vision-based intersection detection for a driver's warning assistant. In: Intelligent Vehicles' 93 Symposium, 1993. IEEE, pp 340–344. doi:https://doi.org/10.1109/IVS.1993.697348

  22. Suzuki S (1985) Topological structural analysis of digitized binary images by border following. Computer vision, graphics, and image processing 30(1):32–46. https://doi.org/10.1016/0734-189X(85)90016-7

    Article  MATH  Google Scholar 

  23. Takahashi A, Ninomiya Y, Ohta M et al (2002) Rear view lane detection by wide angle camera. In: Intelligent Vehicle Symposium, 2002. IEEE, 2002. IEEE, pp 148–153. doi: https://doi.org/10.1109/IVS.2002.1187943

  24. Thrun S, Montemerlo M, Dahlkamp H et al (2006) Stanley: the robot that won the DARPA grand challenge. Journal of field Robotics 23(9):661–692. https://doi.org/10.1002/rob.20147

    Article  Google Scholar 

  25. Wang Y, Teoh EK, Shen D (2004) Lane detection and tracking using B-snake. Image Vis Comput 22(4):269–280. https://doi.org/10.1016/j.imavis.2003.10.003

    Article  Google Scholar 

  26. Yu B, Jain AK (1997) Lane boundary detection using a multiresolution hough transform. In: Image Processing, 1997. Proceedings., International Conference on, 1997. IEEE, pp 748–751. doi:https://doi.org/10.1109/ICIP.1997.638604

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Acknowledgments

This work was supported by the Incheon National University Research Grant in 2015.

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  1. Department of Embedded Systems Engineering, Incheon National University, Incheon, South Korea

    Kyung Yeop Han, Minho Lee & Young-Sup Lee

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  1. Kyung Yeop Han
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Correspondence to Young-Sup Lee.

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Han, K.Y., Lee, M. & Lee, YS. Implementation of autonomous driving of a ground vehicle for narrow high-curvature roads using surround view images. Multimed Tools Appl 79, 8379–8398 (2020). https://doi.org/10.1007/s11042-018-6485-4

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