Art Design and Interior Color Selection for New Energy Vehicles using sustainable AI algorithm

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Yibiao Long, Jia Yong

Abstract

New energy vehicles (NEVs) are becoming increasingly popular in the automotive industry as a sustainable solution to reduce carbon emissions. In order to attract and retain customers, NEVS needs to have visually appealing exterior designs and interior color schemes. The traditional process of selecting art design and interior colors for vehicles can take time and effort. To address this challenge, our team has developed a sustainable AI algorithm for art design and interior color selection for NEVs. This algorithm utilizes machine learning techniques to analyze market trends, customer preferences, and sustainable color choices. The algorithm uses a database of color options and design elements to generate customizable options for NEV manufacturers. It reduces the need for physical prototypes and manual color matching, leading to cost and time savings. The algorithm also takes into consideration the sustainable aspect of NEVs by suggesting color options that are environmentally friendly and promote eco-consciousness.

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