Investigating emotional design of the intelligent cockpit based on visual sequence data and improved LSTM

To enhance affective experience and customer satisfaction in the intelligent cockpit of new energy vehicle (NEV-IC), this article proposes a novel method that combines the visual sequence data of eye movements with the sentiment prediction using improved Long Short-Term Memory (LSTM). Specifically, we used eye-tracking technology to capture users' visual sequence of design morphology for NEV-IC. We then adopted entropy-TOPSIS to compute the ranking of morphological components based on experts' opinions, establishing the coupling between users' visual perception and experts' opinion to obtain the key morphological dataset of NEV-IC based on user visual sequence. To tackle the shortcomings of LSTM, meanwhile, we employed the sparrow search algorithm (SSA) to optimize the hyperparameters of the LSTM model. Moreover, an attention mechanism has been introduced to address LSTM's difficulty in preserving key information when processing the sequential data, enabling a stronger focus on critical sequential features within the user's visual path. To assess the efficacy of the proposed SSA-LSTM-Attention model, a dataset incorporating user emotional imagery was constructed, within the research framework of Kansei engineering (KE). This dataset, in conjunction with the morphological dataset of visual sequential features, was applied to our model. The study results indicated that compared to traditional machine learning models like BP neural network (BPNN), support vector regression (SVR), and LSTM, our model performed better in capturing the nonlinear relationship between user sentiment and design features. Additionally, it exhibited higher predictive accuracy, better generalization ability and stronger robustness.