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Título : Driving assistance algorithm for self-driving cars based on semantic segmentation
Otros títulos : Optics and Photonics for Information Processing XVI
Autor : Rodolfo Macias, Luis
Picos, Kenia
Orozco Rosas, Ulises
Palabras clave : Semantic segmentation;Algorithms
Sede: Campus Tijuana
Fecha de publicación : oct-2022
Citación : vol.12225;
Resumen : This paper presents the implementation of a driving assistance algorithm based on semantic segmentation. The proposed implementation uses a convolutional neural network architecture known as U-Net to perform the image segmentation of traffic scenes taken by the self-driving car during the navigation, the segmented image gives to every pixel a specific class. The driving assistance algorithm uses the data retrieved from the semantic segmentation to perform an evaluation of the environment and provide the results to the self-driving car to help it make a decision. The evaluation of the algorithm is based on the frequency of the pixels of each class, and on an equation that calculates the importance weight of a pixel with its own specific position and its respective class. Experimental results are presented to evaluate the feasibility of the proposed implementation.
metadata.dc.description.url: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12225/2634076/Driving-assistance-algorithm-for-self-driving-cars-based-on-semantic/10.1117/12.2634076.short?SSO=1
URI : https://repositorio.cetys.mx/handle/60000/1497
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