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

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.