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Lane detection techniques for self-driving vehicle: comprehensive review

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Abstract

According to WHO, 1.35 million people, every year are cut short in road accidents, most of them caused due to human misconduct and ignorance. To improve safety over the roads, road perception and lane detection play a crucial part in avoiding accidents. Lane Detection is a constitution for various Advanced Driver Assisting System (ADAS) like Lane Keeping Assisting System (LKAS) and Lane Departure Warning System (LDWS). It also enables fully assistive and autonomous navigation in self-driving vehicles. Therefore, it has been an effective field of research for the past few decades, but various milestones are yet to be achieved. The problem has encountered various challenging scenarios due to the past limitations of resources and technologies. In this paper, we reviewed the different approaches based on image processing and computer vision that have revolutionized the lane detection problem. This paper also summarizes the different benchmark data sets for lane detection, evaluation criteria. We implemented Lane detection system using Unet and Segnet model and applied it on Tusimple dataset. The Unet performance is better as compared to Segnet model. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm. Finally, we compare various researcher’s approaches with their performances. This paper concluded with the challenges to predict accurate lanes under different scenarios.

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References

  1. Alvarez JM, Salzmann M, Barnes N (2014) Large-scale semantic co-labeling of image sets. In: IEEE winter conference on applications of computer vision. IEEE, pp 501–508

  2. Aly M (2008) Real time detection of lane markers in urban streets. In: 2008 IEEE intelligent vehicles symposium. IEEE, pp 7–12

  3. Aly M (2008) Real time detection of lane markers in urban streets. In: 2008 IEEE intelligent vehicles symposium. IEEE, pp 7–12

  4. Assidiq AA, Khalifa OO, Islam MR, Khan S (2008) Real time lane detection for autonomous vehicles. In: 2008 International conference on computer and communication engineering. IEEE, pp 82–88

  5. Aubert D, Kluge KC, Thorpe CE (1991) Autonomous navigation of structured city roads. In: Mobile Robots V. SPIE, vol 1388, pp 141–151

  6. Behrendt K, Soussan R (2019) Unsupervised labeled lane markers using maps. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, pp 0–0

  7. Borkar A, Hayes M, Smith MT (2009) Robust lane detection and tracking with ransac and kalman filter. In: 2009 16th IEEE international conference on image processing (ICIP). IEEE, pp 3261–3264

  8. Broggi A, Cappalunga A, Caraffi C, Cattani S, Ghidoni S, Grisleri P, Porta PP, Posterli M, Zani P (2010) Terramax vision at the urban challenge 2007. IEEE Trans Intell Transp Syst 11(1):194–205

    Article  Google Scholar 

  9. Broggi A, Cattani S (2006) An agent based evolutionary approach to path detection for off-road vehicle guidance. Pattern Recogn Lett 27(11):1164–1173

    Article  Google Scholar 

  10. Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: a high-definition ground truth database. Pattern Recogn Lett 30(2):88–97

    Article  Google Scholar 

  11. Buehler M, Iagnemma K, Singh S (2007) The 2005 DARPA grand challenge: the great robot race. Springer, vol 36

  12. Burrow M, Evdorides H, Snaith M (2003) Segmentation algorithms for road marking digital image analysis. In: Proceedings of the institution of civil engineers-transport. Thomas telford ltd, vol 156, pp 17–28

  13. Caltech-lanes Dataset (2022) https://www.vision.caltech.edu/malaa/datasets/caltech-lanes/. Accessed April 2021

  14. Chen Z, Liu Q, Lian C (2019) Pointlanenet: efficient end-to-end cnns for accurate real-time lane detection. In: 2019 IEEE intelligent vehicles symposium (IV). IEEE, pp 2563–2568

  15. Cheng H-Y, Jeng B-S, Tseng P-T, Fan K-C (2006) Lane detection with moving vehicles in the traffic scenes. IEEE Trans Intell Trans Syst 7 (4):571–582

    Article  Google Scholar 

  16. Fritsch J, Kuehnl T, Geiger A (2013) A new performance measure and evaluation benchmark for road detection algorithms. In: 16th International IEEE conference on intelligent transportation systems (ITSC 2013). IEEE, pp 1693–1700

  17. Ghafoorian M, Nugteren C, Baka N, Booij O, Hofmann M (2018) Elgan: embedding loss driven generative adversarial networks for lane detection. In: Proceedings of the European conference on computer vision (ECCV) workshops, pp 0–0

  18. Guo C, Mita S, McAllester D (2010) Lane detection and tracking in challenging environments based on a weighted graph and integrated cues. In: 2010 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 5543–5550

  19. Gurghian A, Koduri T, Bailur SV, Carey KJ, Murali VN (2016) Deeplanes: End-to-end lane position estimation using deep neural networksa. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 38–45

  20. Haloi M, Jayagopi DB (2015) A robust lane detection and departure warning system. In: 2015 IEEE intelligent vehicles symposium (IV). IEEE, pp 126–131

  21. He B, Ai R, Yan Y, Lang X (2016) Lane marking detection based on convolution neural network from point clouds. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE, pp 2475–2480

  22. He B, Ai R, Yan Y, Lang X (2016) Accurate and robust lane detection based on dual-view convolutional neutral network. In: 2016 IEEE intelligent vehicles symposium (IV). IEEE, pp 1041–1046

  23. He B, Ai R, Yan Y, Lang X (2016) Lane marking detection based on convolution neural network from point clouds. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE, pp 2475–2480

  24. Hou Y (2019) Agnostic lane detection. arXiv:1905.03704

  25. Hou Y, Ma Z, Liu C, Loy CC (2019) Learning lightweight lane detection cnns by self attention distillation. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 1013–1021. https://doi.org/10.1109/ICCV.2019.00110

  26. Huang Y, Li Y, Hu X, Ci W (2018) Lane detection based on inverse perspective transformation and kalman filter. KSII Trans Internet Inf Syst (TIIS) 12(2):643–661

    Google Scholar 

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

    Article  Google Scholar 

  28. Jung CR, Kelber CR (2004) A robust linear-parabolic model for lane following. In: Proceedings. 17th Brazilian symposium on computer graphics and image processing. IEEE, pp 72–79

  29. Kim Z (2008) Robust lane detection and tracking in challenging scenarios. IEEE Trans Intell Trans Syst 9(1):16–26

    Article  Google Scholar 

  30. Kuderer M, Kretzschmar H, Burgard W (2013) Teaching mobile robots to cooperatively navigate in populated environments. In: 2013 IEEE/RSJ Int Conf Intell Robots Syst. IEEE, pp 3138– 3143

  31. Lee S, Kim J, Shin Yoon J, Shin S, Bailo O, Kim N, Lee T-H, Seok Hong H, Han S-H, So Kweon I (2017) Vpgnet: vanishing point guided network for lane and road marking detection and recognition. In: Proceedings of the IEEE international conference on computer vision, pp 1947–1955

  32. Li Y, Iqbal A, Gans NR (2014) Multiple lane boundary detection using a combination of low-level image features. In: 17th International IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 1682–1687

  33. Li J, Mei X, Prokhorov D, Tao D (2016) Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans Neural Netw Learn Syst 28(3):690–703

    Article  Google Scholar 

  34. Liu W, Li S, Huang X (2014) Extraction of lane markings using orientation and vanishing point constraints in structured road scenes. Int J Comput Math 91(11):2359–2373

    Article  MathSciNet  Google Scholar 

  35. Liu G, Wörgötter F, Markelić I (2010) Combining statistical hough transform and particle filter for robust lane detection and tracking. In: 2010 IEEE intelligent vehicles symposium. IEEE, pp 993– 997

  36. Mamidala RS, Uthkota U, Shankar MB, Antony AJ, Narasimhadhan A (2019) Dynamic 0approach for lane detection using google street view and cnn. In: TENCON 2019-2019 IEEE region 10 conference (TENCON). IEEE, pp 2454–2459

  37. McCall JC, Trivedi MM (2004) An integrated, robust approach to lane marking detection and lane tracking. In: IEEE Intelligent vehicles symposium. IEEE, 2004, pp 533–537

  38. Neven D, Brabandere B, Georgoulis S, Proesmans M, Van Gool L (2018) Towards end-to-end lane detection: an instance segmentation approach, pp 286–291. https://doi.org/10.1109/IVS.2018.8500547

  39. Niu J, Lu J, Xu M, Lv P, Zhao X (2016) Robust lane detection using two-stage feature extraction with curve fitting. Pattern Recogn 59:225–233

    Article  Google Scholar 

  40. Ozgunalp U, Dahnoun N (2014) Robust lane detection & tracking based on novel feature extraction and lane categorization. In: 2014 Ieee international conference on acoustics, speech and signal processing (icassp). IEEE, pp 8129–8133

  41. Pan X, Shi J, Luo P, Wang X, Tang X (2018) Spatial as deep: spatial cnn for traffic scene understanding. In: AAAI

  42. Philion J (2019) Fastdraw: addressing the long tail of lane detection by adapting a sequential prediction network, pp 11574–11583. https://doi.org/10.1109/CVPR.2019.01185

  43. Pizzati F, Allodi M, Barrera A, García F (2019) Lane detection and classification using cascaded cnns

  44. Pomerleau D (1995) Ralph: rapidly adapting lateral position handler. In: Proceedings of the intelligent vehicles’ 95. Symposium. IEEE, pp 506–511

  45. Rose C, Britt J, Allen J, Bevly D (2014) An integrated vehicle navigation system utilizing lane-detection and lateral position estimation systems in difficult environments for gps. IEEE Trans Intell Transp Syst 15 (6):2615–2629

    Article  Google Scholar 

  46. Sehestedt S, Kodagoda S, Alempijevic A, Dissanayake G (2007) Robust lane detection in urban environments. In: 2007 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 123–128

  47. Sivaraman S, Trivedi MM (2013) Integrated lane and vehicle detection, localization, and tracking: a synergistic approach. IEEE Trans Intell Transp Syst 14 (2):906–917

    Article  Google Scholar 

  48. Tabelini L, Berriel R, Paixao TM, Badue C, De Souza AF, Oliveira-Santos T (2021) Keep your eyes on the lane: real-time attention-guided lane detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 294–302

  49. Tusimple Dataset (2017) https://github.com/TuSimple/tusimple-benchmark. Accessed April 2021

  50. Veit T, Tarel J-P, Nicolle P, Charbonnier P (2008) Evaluation of road marking feature extraction. In: 2008 11th International IEEE conference on intelligent transportation systems. IEEE, pp 174–181

  51. Veit T, Tarel J-P, Nicolle P, Charbonnier P (2008) Evaluation of road marking feature extraction. In: 2008 11th International IEEE conference on intelligent transportation systems. IEEE, pp 174–181

  52. Von Gioi RG, Jakubowicz J, Morel J-M, Randall G (2008) Lsd: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell 32(4):722–732

    Article  Google Scholar 

  53. Wang Y, Teoh EK, Shen D (2004) Lane detection and tracking using b-snake. Image Vis Comput 22(4):269–280

    Article  Google Scholar 

  54. Wu C-F, Lin C-J, Lee C-Y (2011) Applying a functional neurofuzzy network to real-time lane detection and front-vehicle distance measurement. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(4):577– 589

    Google Scholar 

  55. Xu X, Yu T, Hu X, Ng WW, Heng P-A (2020) Salmnet: a structure-aware lane marking detection network. IEEE Trans Intell Transp Syst 22(8):4986–4997

    Article  Google Scholar 

  56. Yoo S, Lee HS, Myeong H, Yun S, Park H, Cho J, Kim DH (2020) End-to-end lane marker detection via row-wise classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 1006–1007

  57. Yoo JH, Lee S-W, Park S-K, Kim DH (2017) A robust lane detection method based on vanishing point estimation using the relevance of line segments. IEEE Trans Intell Transp Syst 18(12):3254– 3266

    Article  Google Scholar 

  58. Yuan J, Tang S, Pan X, Zhang H (2014) A robust vanishing point estimation method for lane detection. In: Proceedings of the 33rd chinese control conference. IEEE, pp 4887–4892

  59. Zhang Y, Lu Z, Ma D, Xue J-H, Liao Q (2020) Ripple-gan: lane line detection with ripple lane line detection network and wasserstein gan. IEEE Trans Intell Transp Syst 22(3):1532–1542

    Article  Google Scholar 

  60. Zhang G, Zheng N, Cui C, Yan Y, Yuan Z (2009) An efficient road detection method in noisy urban environment. In: 2009 IEEE intelligent vehicles symposium. IEEE, pp 556–561

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Acknowledgements

We thank Dr Swati Shinde, Professor, Pimpari Chinchwad College of Engineering, Pune for her assistance in the revision of this paper. Her comments and suggestions has greatly improved the manuscript.

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    1. Department of Information Technology, Pune, Army Institute of Technology, Dighi, Pune, 411015, Maharashtra, India

      Ashwini Sapkal,  Arti, Dishant Pawar & Prashant Singh

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    1. Ashwini Sapkal
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    All authors have equally contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

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    Sapkal, A., Arti, Pawar, D. et al. Lane detection techniques for self-driving vehicle: comprehensive review. Multimed Tools Appl 82, 33983–34004 (2023). https://doi.org/10.1007/s11042-023-14446-6

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