Exploring relationships between design features and system usability of intelligent car human–machine interface

In-vehicle human–machine interface (HMI) mainly refers to the T-shaped panel system with instruments, centre console, gear lever and other components installed. For intelligent vehicles, the high level of intelligent interconnection may to some extent make drivers lack situational safety awareness and reduce the usability of the system. Thus, this study attempted to establish a relationship between design features and system usability of the in-vehicle panels. From the perspective of visual ergonomics, the panels were deconstructed into design features to determine 36 samples to be studied. After dividing each sample into four areas of interest (AOI), eye movement and subjective preference data were collected to quantify the user experience. Artificial neural network (ANN) and support vector machine (SVM) were used in the study. Nevertheless, conventional learning algorithms often underwent deficiencies in accuracy and robustness in the detection of multifarious kinds of panels. Therefore, the parameters of the two models were tuned to deal with the noise common in user experience data. The determinant coefficients, mean-square errors and mean relative errors of the two models showed that the SVM model had a higher accuracy, smaller error and was more stable in the learning of user experience of HMI design features, which could provide a method for the layout design and evaluation of T-shaped instrument panel.