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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.
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Luis Rodolfo Macias, Kenia Picos, Ulises Orozco-Rosas, "Driving assistance algorithm for self-driving cars based on semantic segmentation," Proc. SPIE 12225, Optics and Photonics for Information Processing XVI, 1222505 (3 October 2022); https://doi.org/10.1117/12.2634076