@inproceedings{Kuk2012,
Author = {Kuka, Christian and Gerwinn, Sebastian and Schweigert, Sören and Eilers, Sönke and Nicklas, Daniela},
Title = {Demo: Context-Model Generation for Safe Autonomous Transport Vehicles},

Year = {2012},
Pages = {365-366},
Month = {07},
Series = {DEBS '12},
Isbn = {978-1-4503-1315-5},
Booktitle = {Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems},
Organization = {ACM},
type = {inproceedings},
note = {Autonomously operating vehicles highly depend on the qual-
ity of its sensors as they have to be aware of its surround-
ings to react appropriately. Currently operating automated
guided vehicles cover only a limited area with their sensors
and therefo},
Abstract = {Autonomously operating vehicles highly depend on the qual-
ity of its sensors as they have to be aware of its surround-
ings to react appropriately. Currently operating automated
guided vehicles cover only a limited area with their sensors
and therefore can only drive at low speeds.   However,  as
more and more sensors are available, it is essential to build
a context-model, which fuses information of different sen-
sors to cover a larger area and allow for an increased level
of  autonomy.   In  this  paper,  we  present  a  context-model
based on a Bayesian occupancy filter which can be queried
via a data stream management system in order to provide
the necessary information at any point in time.  Addition-
ally, the Bayesian filter is pessimistically as it is constructed
such that probability of occupancy is always upper bounded,
to ensure a sufficient level of safety.}
}
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