Autonomously operating vehicles highly depend on the quality of its sensors as they have to be aware of its surroundings 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 sensors 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. Additionally, 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.
['Christian Kuka', 'Sebastian Gerwinn', 'Sören Schweigert', 'Sönke Eilers', 'Daniela Nicklas']
Context-model generation for safe autonomous transport vehicles