The paper discusses the technologies of training radar systems for autonomous electric vehicles

What is it about?

An analysis of the prospects for using neural network technologies in the design and production of electric vehicles is carried out. Devices and systems that ensure autonomous driving of electric vehicles are described. The advantages and disadvantages of various types of sensors, algorithms for processing radar signals using neural networks, methods of adaptation to changing conditions and hybrid approaches that combine traditional algorithms and machine learning are considered. The article describes the principles of detecting objects by radars and other devices. An assessment of the use of neural networks in processing signals received from radars is carried out. Some approaches that combine different types of neural networks are described. Algorithms for combining CNN and RNN for processing spatio-temporal data, using neural networks in radars with adaptation to changing environmental conditions, using neural networks to improve the classification of objects detected by radar and predict their behavior. The paper proposes a hybrid system that can provide high accuracy in processing complex signals while maintaining computational efficiency and highlights the effectiveness of combining traditional signal processing algorithms with neural networks. This approach combines the computational efficiency of traditional methods with the ability of neural networks to process complex nonlinear dependencies. It is concluded that neural networks can significantly improve radar signal processing, including noise filtering, object detection and classification, and prediction of their behavior. This improves the accuracy and reliability of radar systems.

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Photo by Maxim on Unsplash

Photo by Maxim on Unsplash

Why is it important?

The results of the work will increase the level of technology and safety.

Perspectives