@inproceedingsKuk2012, 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.} @COMMENTBibtex file generated on