Research on Form Design of Automotive Dashboard Based on Kansei Engineering

Abstract

Understanding the driver’s emotional needs for the dashboard is crucial to the success of vehicle design. In order to reduce the psychological burden of drivers in obtaining on-board information, the image of automobile dashboard was visually described and quantitatively analyzed with applying Kansei Engineering theory and the principle of Quantitative Class I. The predictive model between the adjectives of vehicle dashboard shape and perceptual image was established by using multiple linear regression analysis method, and the relationship between perceptual image and design elements was analyzed. The results show that the main sensory factors of shape design should be gentle and comfortable in the process of automobile dashboard design. To improve the fit between automobile dashboard design and driver’s perceptual needs by transforming driver’s emotional perception into design elements.

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

“…Kansei engineering is aimed to transform product design elements and quantifed Kansei imageries into design parameters to facilitate design. As a common method for regression analysis and linear correlation models in Kansei engineering, design elements in QTTI are used as multivariate dependent variables for mapping user's Kansei imageries to form multiple linear regression equations and is now widely used in various felds [10][11][12][13].…”

“…However, representative samples and sample classifcation used in that research were only determined by experts themselves, so the results were lack of objectivity [11]. In the research of automotive dashboard, the KJ method was adopted to select the representative samples and morphological analysis was adopted to deconstruct design elements to determine the optimal design solution, but further quantitative analysis should be conducted to establish a design prediction model to provide more data for a better design solution and provide clear design indicators for subsequent design applications [12]. In the research of sunglasses, users' Kansei scaling was collected through the semantic diference method by Ngip, and then a prediction model between design elements and Kansei scaling was further formed with QTTI to provide clear design indicators for developing sunglasses [13].…”

Dynamic Text: How to Effectively Embed Emotions in Text-based Computer-Mediated Communication

“…Then they found the relation of design features with emotional descriptor terms throughout experiments and statistical modeling. Ren et al (2019) used the method in designing the automotive dashboard. They proposed sensual image adjectives that describe dashboards to understand the (Kim and Kaber, 2009).…”

Dashboard Layout Effects on Drivers' Searching Performance and Heart Rate: Experimental Investigation and Prediction

“…Automobile dashboards could be mainly divided into two types: center-locational ones and driver-orientational ones ( 20 ), and the visual ergonomic performance during interaction with the two types of panels was divergent. The dashboard system might produce different effects on the driver's psychological burden and feelings, thus affecting the information acquisition and operation behaviors of the driver in the cabin ( 21 ). The visual recognition efficiency of the form (circle or linear), indicators (pointers or bar graphs), and direction (horizontal or vertical) of the gauges on various instrument panels was different.…”