title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð´Ð»Ñ Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½ÑÑ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÑ ÑÑÐµÐ´ÑÑÐ²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of algorithms for autonomous vehicles
Creators: name: Ð¡ÑÐ¾Ð¹ÐºÐ¾ÑÐºÐ¸, ÐÐ¸ÐºÐ¾Ð»Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½ÑÐ¹ Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»Ñ
ÑÐ¸ÑÑÐµÐ¼Ð° Ð·ÑÐµÐ½Ð¸Ñ
ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
autonomous vehicle
vision system
semantic segmentation
fully convolutional neural networks

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description: This paper discusses the use of computer vision and machine learning algorithms for their use in autonomous vehicles, in particular self-driving cars. In more detail, the work is focused on the task of processing images from a video stream to obtain information about the surrounding environment, contributing to the possibility of further decision-making for controlling actions of the vehicle.
 The aim of the work is to develop a vision system for an autonomous vehicle, which allows to perceive the surrounding environment from a video stream. This system should receive frames from a video stream as input, assuming that the camera is in front of the vehicle and is directed forward, looking at the space in front of the vehicle. As an output, the system should return labeled road data. In addition, the problem of constructing a movement trajectory from analysis of the labeled road data is considered.
In carrying out this work, various methods of image processing were considered based on computer vision and machine learning algorithms. Verification of the work and evaluation of the quality of the algorithms was carried out on the available source video files with high resolution taken on a racing track. The resulting labels extract the road with high quality even under poor conditions and are sufficient for use in subsequent stages of vision systems in autonomous vehicles.