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Implementation of lane detection algorithm for self-driving car on toll road cipularang using Python language
Aziz M.V.G.a, Prihatmanto A.S.a, Hindersah H.a
a Control and Computer System Laboratory, School of Electrical Engineering and Informatics, ITB, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 IEEE.Self-Driving Car research problem requires several sub-topics that need to be discussed more deeply. Such as Deep Learning, Computer Vision, Fusion Sensor, Localization, Control, until Path Planning. All of them are fusion of several fields of study. This paper discusses the results of implementation of lane detection algorithm on toll road Cipularang as parts of self-driving car system. Video image taken using action camera mounted on top of the vehicle, with 1280×720 resolution. Average speed of the vehicle is 100 km per hour. Programming language of image processing using Python 3. Image processing method are a combination of methods of colour region, line selection, canny edge detection, and Hough transform. The result shows this algorithm needed to be add some method that can changing the parameters during day and night adaptively. Because constant parameters can only be used in the same lighting conditions. Overall the implementation method in Python Language can successfully detect the road lane with an accuracy above 90 percent.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Canny edge detection,Car driving,Constant parameters,Image processing – methods,Lane detection,Lighting conditions,Line selections,Research problems[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]canny edge detection,computer vision,Hough transform,lane detection,self-car driving[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGMENT The authors gratefully acknowledge partial funding support from the USAID under SHERA program.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT.2017.8323550[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]