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, maintainingthe delicate balance of primary task vs. secondary tasks while driving. The authors report a MCDMapproach for rank ordering a large heterogeneous set of human-machine interaction technologies; thefinal set consisted of hundred and one candidates. They measured candidate technologies on eightqualitative 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 asmall set of them. The authors’ approach assisted decision makers in exploring the characteristics ofthe 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 thepresence of important criteria trade-offs.
展开▼
