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

Area of Science:

• Human-Computer Interaction
• Intelligent Transportation Systems
• Automotive Engineering

Background:

• Intelligent driving technology necessitates advanced human-machine interface (HMI) design.
• Defining diverse customer requirements (CRs) and ensuring design stability are key challenges.
• Existing HMI design methods lack thorough investigation for intelligent vehicle applications.

Purpose of the Study:

• To propose a novel HMI design methodology for intelligent vehicles.
• To address the challenges of defining customer requirements and ensuring design quality.
• To enhance the safety, simplicity, and efficiency of user-friendly interface creation.

Main Methods:

• Integration of fuzzy Kano features and quality function deployment (QFD).
• Utilization of physiological experiments: eye-tracking, electroencephalogram (EEG), and electrocorticographic activity (ECoG).
• Application within a human-centred design (HCD) framework.

Main Results:

• The proposed methodology is robust, efficient, and rapidly iterative.
• Demonstrated advantages through navigational interface design examples.
• Provides a clear understanding of enhanced HMI design capabilities.

Conclusions:

• The methodology enhances HMI design for intelligent vehicles.
• Facilitates the creation of safer, simpler, and more efficient user-friendly interfaces.
• Offers a widely applicable approach for HMI design challenges.