@article {pub3238,
	title = {Situated speech-based Driver Assistant Systems: The development of a personalized left-turning assistant},
	author = {Martin Ernst Heckmann AND Dennis Orth AND Heiko Wersing AND Dorothea Kolossa},
	year = {2017},
	month = {May},
	abstract = {We recently developed the concept of {\textquoteleft}{\textquoteleft}Assistance on Demand{\textquoteright}{\textquoteright}. This describes an advanced driver assistance system (ADAS) which supports the driver in an inner city scenario only if they ask for assistance. A key element is the control of the ADAS via speech which allows the driver to flexibly formulate his requests for assistance while the situation develops. Our application scenario is turning left at unsignalized urban intersections. After the driver has activated the system via a speech command it monitors the right side traffic and informs about suitable gaps to enter the intersection, just like a co-driver would do. We assume that it depends on the individual driver what a suitable gap is. To test this hyphothesis we have performed a simulator study using CarMaker where 9 participants were turning left in crossing traffic from both sides. We deploy a maximum likelihood method to estimate the smallest accepted gap of each driver, so called critical gap. The results reveal that there is as postulated a significant inter-individual difference in the critical gap between the drivers. These results are a first step for our investigation if personalization can be utilized to increase the effectiveness and usability of such a speech controlled left-turning assistant and will help us in the further design of the system.},
	publisher = {Springer},
	journal = {ATZ - Automobiltechnische Zeitschrift}
}