@article{ref1,
title="FhG-Co-driver: From map-guided automatic driving by machine vision to a cooperative driver support",
journal="Mathematical and computer modelling",
year="1995",
author="Nagel, H.-h. and Enkelmann, W. and Struck, G.",
volume="22",
number="4-7",
pages="185-212",
abstract="A digital road map provides partial knowledge about the operating environment for a road vehicle. If a road vehicle is equipped with a video camera, machine vision approaches can provide knowledge about the actual traffic environment around the vehicle. Experiences with a combination of two such approaches during the commissioning of a van for automatic driving on a private road network are reported, including experiences gathered during subsequent driving experiments on public roads and several improvement cycles for hardware and software. Based on these experiences, a second generation vehicle for automatic driving–a sedan–has been designed and commissioned. It is currently evaluated on public roads. This equipment provides an experimental platform for studying driver-vehicle interactions with the option to automatically evaluate actual traffic situations around the vehicle in real-time. Our equipment thus offers an approach to record and disentangle the multitude of factors which influence the–often subconscious–reactions of a driver. It is our working hypothesis that only an automatic, in-depth understanding of the actual traffic situation facilitates the design of a driver support system which is competent and flexible enough to win acceptance by a wide spectrum of users.<p />",
language="",
issn="0895-7177",
doi="10.1016/0895-7177(95)00133-M",
url="http://dx.doi.org/10.1016/0895-7177(95)00133-M"
}