An MCDM Approach to the Selection of Novel Technologies for Innovative In-Vehicle Information Systems

Abstract

Driving a car is a complex skill that includes interacting with multiple systems inside the vehicle. Today's challenge in the automotive industry is to produce innovative In-Vehicle Information Systems (IVIS) that are pleasant to use and satisfy the costumers' needs while, simultaneously, maintaining the delicate balance of primary task vs. secondary tasks while driving. The authors report a MCDM approach for rank ordering a large heterogeneous set of human-machine interaction technologies; the final set consisted of hundred and one candidates. They measured candidate technologies on eight qualitative criteria that were defined by domain experts, using a group decision-making approach. The main objective was ordering alternatives by their decision score, not the selection of one or a small set of them. The authors' approach assisted decision makers in exploring the characteristics of the most promising technologies and they focused on analyzing the technologies in the top quartile, as measured by their MCDM model. Further, a clustering analysis of the top quartile revealed the presence of important criteria trade-offs.