Sustainable design of new energy vehicle forms based on visual attention sequences and the Kolmogorov-Arnold Transformer
視覚的注意系列とコルモゴロフ・アーノルド変換器に基づくニューエナジービークル形態の持続可能な設計 (AI 翻訳)
Xinhui Kang, Ziteng Zhao, Zimo Chen
🤖 gxceed AI 要約
日本語
本論文は、ニューエナジービークル（NEV）の外観デザインにおいて、視線追跡とKATを統合した持続可能な設計手法を提案する。視線焦点から形態素を抽出し、大規模言語モデルで感情語彙を収集・重み付けした後、KATで焦点特徴と感情の非線形マッピングを行い最適設計パラメータを生成する。従来のニューラルネットワークより速度・精度・ロバスト性で優れ、ユーザー評価も高い。NEV産業の持続可能な発展に貢献する。
English
This paper proposes a sustainable design approach for new energy vehicle (NEV) forms by integrating visual focus sequences with Kolmogorov-Arnold Transformer (KAT). Eye-tracking captures user focus; morphological decomposition is constructed. Emotional vocabulary is refined using a large language model, and weights are calculated via game theory. KAT maps focus features to emotions, generating optimal design parameters. The method outperforms traditional neural networks in speed, accuracy, and robustness, achieving high user ratings, thus supporting sustainable NEV development.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国のNEV市場を背景としたデザイン研究だが、日本においてもEV/NEVの外観デザインが消費者の購買意欲に大きく影響することから、持続可能なデザイン手法として応用可能性がある。ただし、日本のGX政策やSSBJとの直接的な関連は薄い。
In the global GX context
While focused on the Chinese NEV market, this design methodology has global relevance for sustainable product development in the automotive industry. The integration of AI (KAT, LLM) with emotional design offers a novel approach to enhance NEV competitiveness, indirectly supporting the green transition by promoting EV adoption.
👥 読者別の含意
🔬研究者:Researchers in sustainable design and human-computer interaction can leverage this methodology for NEV form optimization.
🏢実務担当者:Automotive designers and NEV manufacturers can apply these methods to create emotionally appealing and competitive sustainable designs.
📄 Abstract（原文）
Against the backdrop of the global green transition and the booming new energy vehicle (NEV) market, the new energy sport utility vehicle (NEV-SUV) has become mainstream for its environmental attributes and performance advantages. The emotional experience conveyed by its appearance has become a key factor influencing purchase decisions. To achieve sustainable NEV-SUV form design, this study proposes an innovative approach integrating visual focus sequences with Kolmogorov-Arnold Transformer (KAT). Eye-tracking captures users’ focus points, then a morphological decomposition table of the NEV-SUV’s appearance is constructed. Core emotional vocabulary is collected and refined using a large-scale language model, followed by calculation of vocabulary weights via an improved game-theoretic method. The top three words are selected, and KAT establishes a nonlinear mapping between NEV-SUV focus features and emotional vocabulary, generating optimal design parameter combinations. Finally, Rhino is used for 3D modelling, with generative AI for fast rendering and point cloud testing to verify accuracy. Results show the final design received high user ratings, and compared with traditional neural networks, KAT better captured emotional features, with advantages in speed, accuracy, and robustness. The findings provide scientific data support for NEV-SUV appearance design, enhancing competitiveness and promoting sustainable development in the NEV industry.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/09544828.2026.2698391first seen 2026-07-08 05:25:08
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。