@inproceedings {pub4603,
	title = {Asymmetry-based behavior planning for cooperation at shared traffic spaces},
	author = {Raphael Wenzel AND Malte Probst  AND Thomas H Weisswange AND Tim Puphal AND Julian Eggert},
	year = {2021},
	month = {July},
	abstract = {This work is concerned with cooperative behavior planning in a class of scenarios called shared traffic space scenarios. The proposed method does not rely on car-to-car communication and can therefore be used in mixed-traffic as well. Shared traffic space scenarios share two characteristics: First, two agents compete for a limited resource (e.g. road space, place in an order) and second, the agents have to allocate those resources themselves by choosing one of multiple safe behavior options. State-of-the-art behavior planners, using only the most likely prediction of the other vehicle, inherently fail to account for the multi-modal nature of these scenarios. We propose an addition to existing behavior planners based on asymmetry, which accounts for the first-come-first-serve rule usually displayed by human drivers in those scenarios. By measuring the situations asymmetry, a preferable order is determined and executed under fallback-safeguarded behavior planning. Furthermore, the asymmetry progression is shown to be an effective measure of if the agent{\textquoteright}s behavior leads to a resolution of the scenario and the mutual agreement on an preferred outcome. },
	publisher = {IEEE},
	booktitle = {IEEE Intelligent Vehicle Symposium 2021}
}