Abstract
As an important role in autonomous vehicles or advanced driving assistance systems, lane detection uses the onboard camera high up on the windshield to provide the vehicle’s lateral offset within its own lane in a real-time, low-cost way. In this paper, we propose an efficient, robust lane detection method based on histogram of oriented vanishing points. First, the lane features are extracted by symmetrical local threshold. Then, the lines are generated from oriented vanishing points. The lines crossing most features are selected and oriented vanishing points are updated by the overlap between features and selected lines. Last step will be repeated for getting stable oriented vanishing points. Therefore, the last selected lines are most likely to be lane lines. Finally, Validate and select the best lane lines. The proposed method has been tested on a public dataset. The experimental results show that the method can improve robustness under real-time automated driving.
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Acknowledgement
This research was funded by National Natural Science Foundation of China (41671441, 41531177, U1764262).
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Chen, S., Li, B., Guo, Y., Zhou, J. (2020). Lane Detection Based on Histogram of Oriented Vanishing Points. In: Cree, M., Huang, F., Yuan, J., Yan, W. (eds) Pattern Recognition. ACPR 2019. Communications in Computer and Information Science, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3651-9_1
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DOI: https://doi.org/10.1007/978-981-15-3651-9_1
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