@article{ref1,
title="From data to information: Probabilistic methods for dealing with sensor inaccuraty",
journal="VDI Berichte",
year="2006",
author="Gerdes, A",
volume="2006",
number="1960",
pages="543-552",
abstract="In order to efficiently assist the driver, assistance systems need to be able to tune the offered assistance to the driver's needs. As the number and variety of assistance systems in the automobile increase, we are increasingly supplied with data from various sensors about the state of the vehicle, the driver and the driving environment. A fusion of the available data can provide the systems with a valuable overview of the driving situation, which can be used for situation-based tuning of the assistance offered. The raw data returned by several of the sensors is, however, intrinsically uncertain, making a simple, rule-based evaluation of the data impractical. Computations based on probability theory are therefore used to draw inferences from the uncertain data available. The probability value assigned to the inference drawn offers an assistance system an indication of the level of confidence that may be associated with the information received.  <p>Language: de.</p>  <p></p>",
language="de",
issn="0083-5560",
doi="",
url="http://dx.doi.org/"
}