title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐµÑ Ð½Ð¸ÐºÐ¸ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐ°ÐµÐºÑÐ¾ÑÐ¸Ð¸ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ
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: Algorithm for the application of automatic driving technology and predicting the trajectory of movement
Creators: name: Ð¡Ðµ Ð§Ð¶ÑÐ°Ð½
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: 2021
Subjects: Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ðµ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÐ°ÐµÐºÑÐ¾ÑÐ¸Ð¸ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
automatic driving
trajectory forecasting
machine learning
computer vision

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description: Qualification work on the topic: "Algorithm for the application of driving techniques and predicting the trajectory of movement". The scientific project reflects the use of algorithms for a computer vision system and a subsection of artificial intelligence - machine learning. Their application is to establish factors for the use of unmanned vehicles or other vehicles without a driver. Particular attention was paid to issues such as video content processing. This is required for the acceptance of data on the external environment. This will provide an opportunity in the future to solve questions about the controlled maneuvers of the car. The creation of a full-fledged vision system for a car without a driver, thanks to which it is possible to easily perceive the environment from a video stream, is the goal of this qualification work. Receiving frames from a video stream at the input. In this case, the localization of the camera should be in front of the vehicle, orientation - forward, looking at the place in front of the vehicle. At the output, the system will inform the track data - marking. We also consider the option of creating a trajectory when examining the marked highway data. As a result of the work, the types and methods of image processing were considered in order to select a strategy for computer vision and machine learning. The evaluation and quality of the algorithms was checked on video files on which the shooting of the race track was recorded. The recorded data has a high resolution, which allows you to view in detail all the marks and other important points. The marks that are used to highlight the road show the road reasonably well even under the most unfavorable climatic conditions. They can be used in subsequent processes of vision systems in unmanned vehicles.