Big Data-Powered End User Function Development (BIG FUN)

Purpose and goal:

The BIG FUN project aims to understand how to apply quantitative analytic methods to identify moments of interest in real-world vehicle journeys. The combination of these findings with advanced qualitative analytic methods will generate actionable insights such as a deeper understanding of challenges and opportunities for improving truck function, feature and service design to better suit commercial mobility needs.

Expected results and effects:

Expected results of Big Fun are to 1. Identify potential moments of significance in vehicle journeys by training algorithms on rules that combine existing expert domain knowledge and previous relevant research; 2. Perform UX research on how to design a service that enables human experts to use the algorithm-generated moments of significance to support qualitative research that generates design knowledge for the improvement of commercial mobility and; 3. Create a demonstrator that showcases the use of AI-powered UX insights in context.

Approach and implementation:

The research plan for Big Fun is to: 1. collect requirements and existing knowledge about ML technology that can be used on vehicle data and needs and requirements of UX experts in the truck domain; 2. iteratively adapt suitable ML technology and UX methods to combine AI and human competence for designing better commercial mobility systems, and; 3. design and evaluate a demonstrator that illustrates how to combine human and AI outcomes for the design of better commercial mobility.