A lane detection algorithm based on reliable lane markings

Date

2018-05-05

Author

Yeniaydın, Yasin
Schmidt, Klaus Verner

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper proposes a robust and effective vision-based lane detection approach. First, two binary images are obtained from the region of interest of gray-scale images. The obtained binary images are merged by a novel neighborhood AND operator and then transformed to a bird's eye view (BEV) via inverse perspective mapping. Then, gaussian probability density functions are fit to the left and right regions of a histogram image acquired from the BEV. Finally, a polynomial lane model is estimated from the identified regions. Experimental results show that the proposed method accurately detects lanes in complex situations including worn-out and curved lanes.

Subject Keywords

Vision-based lane detection, Neighborhood AND operator, Maximum likelihood estimation, Parallelism of lanes

URI

https://hdl.handle.net/11511/43263

DOI

https://doi.org/10.1109/siu.2018.8404486

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

Y. Yeniaydın and K. V. Schmidt, “A lane detection algorithm based on reliable lane markings,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43263.