Abstract
We present an application of HUGIN to solve problems related to diagnosis and control of autonomous vehicles. The application is based on a distributed architecture supporting diagnosis and control of autonomous units. The purpose of the architecture is to assist the operator or piloting system in managing fault detection, risk assessment, and recovery plans under uncertainty. To handle uncertainty, we focus on the use of probabilistic graphical models (PGMs) as implemented in the HUGIN tool.
Cite
CITATION STYLE
van Gerven, M., & Lucas, P. J. F. (2007). Advances in Probabilistic Graphical Models. In StudFuzz 213 (pp. 377–396). Berlin Heidelberg: Springer-Verlag. Retrieved from papers2://publication/uuid/A467EE3E-74B7-4370-AA93-FF8102C6004A
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