@inproceedings {pub4646,
	title = {Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior Generation Framework},
	author = {Malte Probst  AND Raphael Wenzel AND Tim Puphal AND Thomas H Weisswange AND Nico Andreas Steinhardt AND Bram Bolder AND Benedict Flade AND Julian Eggert},
	year = {2021},
	month = {September},
	abstract = {The task of driving autonomously is difficult due
to the vast number of driving situations a system may be
facing. Especially higher levels of automation in less restricted
scopes remain a topic of active research. In previous work,
we introduced a behavior planning system which uses analytic
models to evaluate the quality of behavior holistically. It uses
these models to generate quality-maximizing behavior instead
of selecting among predefined behavior primitives. The system
was able to solve various complex urban traffic scenarios in
large-scale simulations. In this paper, we verify the system using
multiple prototype vehicles on proving grounds in a number of
difficult urban scenarios such as prioritized intersections or
overtaking. We describe the system architecture and principles which render the system embodiment-agnostic and make
extensions for additional features possible without massively
increasing the complexity.},
	publisher = {IEEE},
	booktitle = {IEEE Intelligent Transportation Systems Conference 2021}
}