title: ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾Ð³Ð¾ Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½Ð¾Ð³Ð¾ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½Ð¾Ð³Ð¾ ÑÑÐµÐ´ÑÑÐ²Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Neural network training to ensure safe driving of an unmanned vehicle
Creators: name: Ð¡ÐµÐ¼ÐµÐ½Ð¾Ð², ÐÐµÐ¾Ð½Ð¸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: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð°Ð²ÑÐ¾Ð½Ð¾Ð¼Ð½Ð¾Ðµ Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ðµ
Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½ÑÐµ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÐµ ÑÑÐµÐ´ÑÑÐ²Ð°
Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ
ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð´Ð°Ð½Ð½ÑÐµ
ÑÐ¸Ð¼ÑÐ»ÑÑÐ¸Ð¸
deep learning
computer vision
autonomous driving
unmanned vehicles
security
synthetic data
simulations

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description: The thesis was completed on the topic: "Training a neural network to ensure safe driving of an unmanned vehicle". The purpose of the work is to develop and train a neural network to ensure safe driving of unmanned vehicles. The first chapter provides a review of the literature, which examines the theoretical foundations and modern methods of deep learning and computer vision, as well as a review of research in the field of unmanned vehicles. The second chapter discusses the main approaches to deep learning and computer vision, loss and optimization methods, as well as data sources for training and testing neural networks. The third chapter describes materials and methods, including methods for evaluating and selecting model parameters, tools and libraries for implementing models, as well as security measures. The fourth chapter presents the implementation of the model based on deep learning, including conducting experiments on various datasets, analyzing the influence of parameters on the quality of models and presenting experimental results. In conclusion, conclusions are drawn on the work done, the need to integrate various sensory data and deep learning methods to create a reliable autonomous vehicle control system is justified.