Is devoted to the development of electronic systems based on the use of neural networks.

What is it about?

The article discusses the role of neural networks in autonomous driving systems for electric vehicles, focusing on the use of various neural network architectures. Several neural networks are considered, such as PointNet, Dynamic Graph CNN (DGCNN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Autoencoders. The composition of neural networks and the advantages of their use for processing data from sensors, cameras, lidars and radars are presented, which makes it possible to increase the safety and efficiency of self-driving electric vehicles. The paper describes the applicability of autoencoders, which can be used to improve the quality of data obtained from sensors such as cameras, radars and lidars. The article describes the main types of autoencoders used to provide autonomous driving systems for electric vehicles. Specific tasks are considered, such as object recognition, noise filtering, route optimization and adaptation to changing conditions.

Featured Image

Why is it important?

The article emphasizes the need for further research and development of neural network algorithms to improve the reliability and stability of autonomous systems in modern conditions.

Perspectives