Lane line detection and departure estimation in a complex environment by using an asymmetric kernel convolution algorithm

Deep learning has made tremendous advances in the domains of image segmentation and object classification. However, real-time lane line detection and departure estimates in complex traffic conditions have proven to be hard in autonomous driving research. Traditional lane line detection methods requi...

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Veröffentlicht in:	The Visual computer Jg. 39; H. 2; S. 519 - 538
Hauptverfasser:	Haris, Malik, Hou, Jin, Wang, Xiaomin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Artificial neural networks Asymmetry Automobile safety Cameras Classification Computer Graphics Computer Science Deep learning Driving conditions Estimates Human error Image Processing and Computer Vision Image segmentation Machine learning Neural networks Object recognition Original Article Parameter modification Pavement deterioration Real time Roads & highways Sensors Teaching methods Traffic Traffic accidents & safety Asymmetric kernel CNN (AK-CNN) Lane line detection Lane departure estimation CULane dataset Scale perception
ISSN:	0178-2789, 1432-2315
Online-Zugang:	Volltext
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