Driving assistance algorithm for self-driving cars based on semantic segmentation

Luis Rodolfo Macias; Kenia Picos; Ulises Orozco-Rosas

doi:10.1117/12.2634076

3 October 2022 Driving assistance algorithm for self-driving cars based on semantic segmentation

Luis Rodolfo Macias, Kenia Picos, Ulises Orozco-Rosas

Proceedings Volume 12225, Optics and Photonics for Information Processing XVI; 1222505 (2022) https://doi.org/10.1117/12.2634076
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Luis Rodolfo Macias, Kenia Picos, and 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

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