Towards a Multi-hypothesis Road Representation for Automated Driving

The process of perception inevitably involves uncertainty. In the field of automated driving, however, uncertainty about the roadway - and especially about the understanding of the roadway - is yet mostly neglected. In this paper it is argued that this uncertainty should be represented and considered in the process of behavior generation. A road representation capable of representing multiple hypotheses about the roadway is presented. The representation language allows to express where and how hypotheses differ and to infer this information in an efficient way. The focus of this paper is on the qualitative representation of uncertainty.