Human-robot interaction: a survey
Foundations and Trends in Human-Computer Interaction
Coupling control and human factors in mathematical models of complex systems
Engineering Applications of Artificial Intelligence
Guest editorial: introducing perception, planning, and navigation for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Haptic car-following support with deceleration control
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Active deceleration support in car following
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
Automated driving aids: modeling, analysis, and interface design considerations
Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
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Human-centered automation problems have multiple attributes: an attribute reflecting human goals and capabilities, and an attribute reflecting automation goals and capabilities. In the absence of a general theory of human interaction with complex systems, it is difficult to define and find a unique optimal multiattribute resolution to these competing design requirements. We develop a systematic approach to such problems using a multiattribute decomposition of human and automation goals. This paradigm uses both the satisficing decision principle which is unique to two-attribute problems, and the domination principle which is a common manifestation of the optimality principle in multiattribute domains. As applied to human-centered automation in advanced vehicle systems, the decision method identifies performance evaluations and compares the safety benefit of a system intervention against the cost to the human operator. We illustrate the method by analyzing an automated system to prevent lane departures