Road Lane Detection System for Self Driving Car

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Rakesh Kadu Rakesh Kadu
Purushottam J. Assudani
Manvi Jaiswal
Druvsingh Bist
Ankush Tickoo

Abstract

Self-driven car or driverless car is an innovation in the Automobile industry. In a self-driven car, direction requires automation. The detection of road lanes or boundaries is a complex and challenging process. In this process identification of the road and the vehicle position on the road plays an important role. It includes the identification of the road and finding the position of the vehicle and road. This paper presents an image-based road lane detection system for self-driven cars using a front camera. The camera mounted on the front dashboard will capture a video stream and apply the processing to detect the road lane. The images will be extracted from continuously captured video and will apply various image processing algorithms like greyscale, Gaussian blur, Canny edge detection, Hough transform to detect and construct the lane which is used to guide the car on road. The system will generate the video based on a processed image which is used to further control the car movement.


 

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How to Cite
Rakesh Kadu, R. K., Purushottam J. Assudani, Manvi Jaiswal, Druvsingh Bist, & Ankush Tickoo. (2021). Road Lane Detection System for Self Driving Car. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.466

References

  1. Saha, Anik, Dipanjan Das Roy,Tauhidul Alam, and Kaushik Deb, 2018. Automated Road Lane Detection for Intelligent Vehicles. Global Journal of Computer Science and Technology Volume 12 Issue 6 Version 1.0 March 2012, pp.596–602.
  2. Tseng, Chien-Cheng, Hsu-Yung Cheng, and Bor-Shenn Jeng 2019. A lane detection using geometry information and modified Hough transform 18th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP 2005) 2005/8/21 23, Taipei, ROC, pp.796–802.
  3. Xiaodong Miao, Shunming Li, and Huan Shen 2012. On-board lane detection system for intelligent vehicle based on monocular vision. in International Journal on Smart Sensing and Intelligent Systems 12, no. 4 (2012),pp.13–16. DOI: https://doi.org/10.21307/ijssis-2017-517
  4. Zhao, Hongying, Zhu Teng, Hong-Hyun Kim, and Dong-Joong Kang, 2013. Annealed Particle Filter Algorithm Used for Lane Detection and Tracking. Journal of Automation and Control Engineering vol. 14, no. 10, pp.4674–4682.
  5. Anjali Goel 2018. Lane Detection Techniques - A Review. International Journal of Computer Science and Mobile Computing Vol.3 Issue.2, February- 2014, pp.596–602.
  6. Farag, Wael, Saleh, and Zakaria, 2018. Road Lane-Lines Detection in Real-Time for Advanced Driving Assistance Systems. 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) doi: 10.1109/3ICT.2018.8855797, pp.1–8. DOI: https://doi.org/10.1109/3ICT.2018.8855797
  7. M DhanaLakshmi , and B.J. Rani Deepika, 2018. A brawny multicolor lane coloration method to Indian scenarios. International Journal of Research in Engineering and Technology Vol.1, Issue 2, ISSN 2319-1163, pp.202–206.
  8. Marzougui, Mehrez & Alasiry, Areej & Kortli, Yassin & Baili, and Jamel, 2020. A Lane Tracking Method Based on Progressive Probabilistic Hough Transform. IEEE Access 10.1109/Anti-Cybercrime.2017.7905298, pp.1–1. DOI: https://doi.org/10.1109/ACCESS.2020.2991930
  9. W. Rong, Z. Li, W. Zhang , and L. Sun, 2014. An improved Canny edge detection algorithm. 2014 IEEE International Conference on Mechatronics and Automation, 2014 doi: 10.1109/ICMA.2014.6885761, pp.577–582. DOI: https://doi.org/10.1109/ICMA.2014.6885761
  10. L. Xu, and E. Oja, 1993. Randomized Hough Transform (RHT): Basic Mechanisms, Algorithms, and Computational Complexities. CVGIP Image Understanding Volume 57, Issue2, ISSN 1049-9660, pp.131–154. DOI: https://doi.org/10.1006/ciun.1993.1009