Research on user correlation of intelligent connected vehicles based on convolutional neural network

Baili Yang; Josefine De Leon

doi:10.1117/12.3004554

11 October 2023 Research on user correlation of intelligent connected vehicles based on convolutional neural network

Baili Yang, Josefine De Leon

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280050 (2023) https://doi.org/10.1117/12.3004554
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

With the rapid development of intelligent connected vehicles, user relevance is becoming more and more important to automakers and service providers. This paper presents a novel approach for intelligent user relationship management in the context of intelligent connected vehicles using Convolutional Neural Networks (CNN). The method uses CNN to extract features from customer data, including driving behavior, preferences, and feedback, and then uses these features to predict customer needs and preferences. Design and implement a prototype intelligent customer relationship management system, combined with the proposed method, to provide users with personalized services and support. Experimental results show that the proposed method can effectively improve the accuracy of predicting customer needs and preferences, and the proposed system can provide customers with better customized services. The research results have important practical significance for the development of the intelligent networked automobile industry and the improvement of customer satisfaction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Baili Yang and Josefine De Leon "Research on user correlation of intelligent connected vehicles based on convolutional neural network", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280050 (11 October 2023); https://doi.org/10.1117/12.3004554

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KEYWORDS

Convolutional neural networks

Computer security

Analytical research

Convolution

Feature extraction

Network security

Reliability

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