An automotive human-machine interface design method integrating Fuzzy Kano-QFD and physiological data

Against the backdrop of escalating intelligent driving technology, the challenge for human-machine interface (HMI) design is to accurately define diverse and individualised customer requirements (CRs), as well as to ensure the stability, usability and competitive advantage of the design solution. HMI design methods that address these issues have not been thoroughly studied. To address this challenge, this study proposes a HMI design methodology that integrates fuzzy Kano features, quality function deployment (QFD) and physiological experiments (eye-tracking, electroencephalogram and electrocorticographic activity) within a human-centred design (HCD) framework. The method is robust, efficient, rapidly iterative and widely applicable in HMI design. The advantages of the proposed methodology have been demonstrated and evaluated with examples of navigational interface design to give a clear understanding. This methodology will enhance HMI design and make creating user-friendly interfaces for intelligent vehicles safer, simpler and more efficient.