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Road information collector using smartphone for measuring road width based on object and lane detection
Nasution S.M.a, Husni E.a, Kuspriyanto K.a, Yusuf R.a, Mulyawan R.a
a Institut Teknologi Bandung, 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]© 2020 International Association of Online Engineering.Change in traffic condition is unpredictable. This increases the need for alternate routes to avoid congestions and other conditions. The high variance of vehicle types in Indonesia complicates routing, rendering alternative routes sometimes become unavailable for a specific condition of vehicles. Our research is to develop an application for Android smartphone to collect road information and to offer alternative routes for motorcycles; this paper focuses on the first part of the task. The needed information to acquire is road width, so the drivers could use proper alternative routes for their vehicles (e.g. small car or motorcycles). This research uses both object detection and lane detection methods for obtaining road width, and it is quite simple when lane boundaries are detected in road image. When the lane boundaries are not detected, road width is obtained using a vanishing point method. The average error rate for road width measurement using object and lane detection is 19.71%. Meanwhile, the average error rate when there is no lane boundary is in the range of 10-15%, 8-18%, and 10-19% for various capturing sides. Reclassification of the road is done when the error rate of road width is set. Accuracy of road category reclassification is in the range of 70-75% in various sides.[/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][/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]Lane detection,Object detection,Road information,Road width,Vanishing point[/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][/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/10.3991/ijim.v14i02.11530[/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]