@inproceedings {pub3932,
	title = {Behavior investigation of a risk-aware driving model for trajectory prediction},
	author = {Fabian M{\"u}ller AND Julian Eggert},
	year = {2019},
	month = {June},
	abstract = {The prevention of risky situations is one of the main tasks in autonomous driving and intelligent driving as-
sistant systems. Uncertainty in the traffic participants behavior and sensor noise leads to critical situations, which
have to be anticipated by appropriate risk prediction approaches. The risk prediction itself requires dedicated
driver models which are interaction sensitive and computationally cheap, to efficiently simulate how a scene
might evolve. In this paper, we present a new driver model which is aware of the usual risks encountered in
normal driving scenarios. It can cope with longitudinal as well as lateral collision risks, and adjusts its behavior
by minimizing the expected integral risk over the future time in a gradient-like fashion. We show that our model
is suited for the prediction of parallel lane scenarios like overtaking, following and in-between positioning and
anlayze its behavior and its stability.},
	publisher = {Fast-Zero},
	booktitle = {FAST-zero 2019}
}