A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

Main Article Content

Vidya Sagar S D
Prabhakar C J

Abstract

Intelligent driving systems need to find the lines that show where the lanes are present. It can help drivers prevent lane hopping and improve vehicle positioning and identification by providing information about the current road conditions. The lane detection encounters several obstacles, including harsh illumination conditions, missing lane markings, and impediments. Due to their outstanding performance, Artificial Intelligence, machine learning, and deep learning-based algorithms have recently attracted considerable interest in the intelligent driving society. In this paper, we thoroughly analyses different lane detection approaches for lane detection including deep learning based techniques. In addition, we review known datasets about lanes and assessment criteria. It ends with a discussion of current problems and possible directions for a lane detection system.

Article Details

How to Cite
[1]
V. S. S D and P. C J, “A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 8, pp. 1–10, Aug. 2023.
Section
Research Articles

References

Zhang, J., Deng, T., Yan, F., & Liu, W. (2021). “Lane detection model based on spatio-temporal network with double convolutional gated recurrent units”. IEEE Transactions on Intelligent Transportation Systems, 23(7), 6666-6678.

Zhai, G. “Real time lane detection model based on Lightweight”. In Proceedings of the 2020 4th International Conference on Video and Image Processing (pp. 13-18).

Wang, W., Lin, H., & Wang, J. (2020). “CNN based lane detection with instance segmentation in edge-cloud computing”. Journal of Cloud Computing, 9, 1-10.

Wang, Jianzhuang, et al. “Model-based lane detection and lane following for intelligent vehicles.” 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. Vol. 2. IEEE, 2010.

Low, C. Y., Zamzuri, H., &Mazlan, S. A. “ Simple robust road lane detection algorithm”. In 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS) (pp. 1-4). Ieee.

Y. Wang, E. K. Teoh and D. Shen, “Lane detection using B-snake,” International Conference on Information Intelligence and Systems, IEEE, pp. 438-443, 1999.

D. Špoljar, M. Vranješ, S. Nemet and Pjevalica, “Lane Detection and Lane Departure Warning Using Front View Camera in Vehicle,” International Symposium ELMAR, IEEE, pp. 59-64, 2021.

Zhou, S., Jiang, Y., Xi, J., Gong, J., Xiong, G., & Chen, H. (2010, June). “A novel lane detection based on geometrical model and gabor filter”. In 2010 IEEE Intelligent Vehicles Symposium (pp. 59-64). IEEE.

Z. Zhang, “A flexible new technique for camera calibration”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.11, pages 1330-1334, 2000.

Christopher Rasmussen. “Grouping Dominant Orientations for Ill-Structured Road Following”. In Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004.

B. Akbari, J. Thiyagalingam, R. Lee, and K. Thia, ‘‘A multilane tracking algorithm using IPDA with intensity feature,’’ Sensors, vol. 21, no. 2, p. 461, 2021.

F. Mariut, C. Fosalau and D. Petrisor, “Lane Mark Detection Using Hough Transform”, In IEEE International Conference and Exposition on Electrical and Power Engineering, pp. 871 - 875, 2012.

T. T Tran, C. S Bae, Y. N. Kim, H.M. Cho, and S.B. Cho, “An Adaptive Method for Lane Marking Detection Based on HSI Color Model”, ICIC, CCIS 93, pp. 304– 311, 2010.

K. Ghazali, R. Xiao and J. Ma, “Road Lane Detection Using H-Maxima and Improved Hough Transform”, Fourth International Conference on Computational Intelligence, Modelling and Simulation, pp: 2166-8531, 2012.

S. Srivastava, R. Singal and M. Lumb, “ Efficient Lane Detection Algorithm using Different Filtering Techniques”, International Journal of Computer Applications, vol. 88, no.3, pp. 975-8887, 2014.

O.O. Khalifa and A.H.A Hashim, “Vision-Based Lane Detection for Autonomous Artificial Intelligent Vehicles”, In IEEE International Conference on Semantic Computing, pp. 636 - 641, 2009.-------

D. Pomerleau and T. Jochem, “Rapidly adapting machine vision for automated vehicle steering,” IEEE expert, vol. 11, no. 2, pp. 19-27, 1996.

A. Mammeri, G. Lu and A. Boukerche, “Design of lane keeping assist system for autonomous vehicles,” 7th International Conference on New Technologies, Mobility and Security (NTMS), IEEE, pp. 1-5, 2015.

Y. Chen, “A Novel Lightweight Lane Departure Warning System Based on Computer Vision for Improving Road Safety,” Doctoral dissertation, Université d’Ottawa/University of Ottawa, Ottawa, 2021.

W. Farag and Z. Saleh, “Road lane-lines detection in real-time for advanced driving assistance systems,” International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), IEEE, pp. 1-8, 2018.

S. Zhou, Y. Jiang, J. Xi, J. Gong and G. Xiong, “A novel lane detection based on geometrical model and gabor filter,” IEEE Intelligent Vehicles Symposium, pp. 59-64, 2010.

Y. L. Chen, B. F. Wu, H. Y. Huang and C. J. Fan, “A real-time vision system for nighttime vehicle detection and traffic surveillance,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2030-2044, 2010.

A. M. Kumar and P. Simon, “Review of lane detection and tracking algorithms in advanced driver assistance system,” Int. J. Comput. Sci. Inf. Technol, vol. 7, no. 4, pp. 65-78, 2015.

W. Zhang, X. Song, S. Zhang and X. Wu, “Real-time Lane Recognition Method Based on Hardware-software Co-design,” China Mechanical Engineering, vol. 26, no. 10, p. 1337, 2015.

Y. Xing, C. Lv, L. Chen and H. Wang, “Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 3, p. 64, 2018.

L. Yahiaoui, J. Horgan, B. Deegan and S. Yogamani, “Overview and empirical analysis of isp parameter tuning for visual perception in autonomous driving,” Journal of Imaging, vol. 5, no. 10, p. 78, 2019.

W. Chen, W. Wang, K. Wang, Z. Li and H. Li, “Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review,” Journal of traffic and transportation engineering, vol. 7, no. 6, pp. 748-774, 2020.

S. K. Satti, K. S. Devi, P. Dhar and Srinivasan, “A machine learning approach for detecting and tracking road boundary lanes,” ICT Express, pp. 99-103, 2021.

Chougule, S., Koznek, N., Ismail, A., Adam, G., Narayan, V., & Schulze, M. (2018). “Reliable multilane detection and classification by utilizing CNN as a regression network”. In Proceedings of the European conference on computer vision (ECCV) workshops .

Zhang, J., Xu, Y., Ni, B., & Duan, Z. (2018). “Geometric constrained joint lane segmentation and lane boundary detection”. In proceedings of the european conference on computer vision (ECCV) (pp. 486-502).

John, V., Karunakaran, N. M., Guo, C., Kidono, K., & Mita, S. (2018, August). “Free space, visible and missing lane marker estimation using the PsiNet and extra trees regression”. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 189-194). IEEE.

Singal, G., Singhal, H., Kushwaha, R., Veeramsetty, V., Badal, T., & Lamba, S. (2023). “RoadWay: lane detection for autonomous driving vehicles via deep learning”. Multimedia Tools and Applications, 82(4), 4965-4978.

Pizzati, F., Allodi, M., Barrera, A., & García, F. (2020). “Lane detection and classification using cascaded CNNs. In Computer Aided Systems Theory–EUROCAST” 17th International Conference, Las Palmas de Gran Canaria, Spain, February 17–22, 2019, Revised Selected Papers, Part II 17 (pp. 95-103). Springer International Publishing.

https://opendatalab.com/tusimple_lane

Fritsch, J., Kuehnl, T., & Geiger, A. (2013, October). “A new performance measure and evaluation benchmark for road detection algorithms” In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) (pp. 1693-1700). IEEE.

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