What question did this study set out to answer?

This research aims to develop a framework that accurately translates Kansei needs into interior design features for New Energy Vehicles.

Kansei analysis for interior design of new energy vehicles: combining rough set theory, eye tracking, and improved gated recurrent unit

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Randomized trial integrates design features through advanced algorithms to enhance NEV cabin experience.

This research aims to develop a framework that accurately translates Kansei needs into interior design features for New Energy Vehicles.
Proposed an Attention-Aware Rough Set Feature Selection Method (ARFS) using Rough Set Theory for feature reduction.
Integrated Eye-Tracking technology to capture and analyze visual search patterns.
Used a TCN-BiGRU-Attention deep learning model for mapping NEV design features to Kansei imagery.
ARFS improved prediction accuracy over standard models, showcasing the effectiveness of the approach (p<0.01).
The uncertainty-aware recommendation mechanism provides a diverse candidate set of design options, balancing creativity and reliability.
5-fold cross-validation showed significant transferability of ARFS across various NEV interior components.

Wang et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial integrates design features through advanced algorithms to enhance NEV cabin experience.

This research aims to develop a framework that accurately translates Kansei needs into interior design features for New Energy Vehicles.
Proposed an Attention-Aware Rough Set Feature Selection Method (ARFS) using Rough Set Theory for feature reduction.
Integrated Eye-Tracking technology to capture and analyze visual search patterns.
Used a TCN-BiGRU-Attention deep learning model for mapping NEV design features to Kansei imagery.
ARFS improved prediction accuracy over standard models, showcasing the effectiveness of the approach (p<0.01).
The uncertainty-aware recommendation mechanism provides a diverse candidate set of design options, balancing creativity and reliability.
5-fold cross-validation showed significant transferability of ARFS across various NEV interior components.

Wang et al. (2026) studied this question.

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