Using Sum-Product Networks for the Generation of Vehicle Populations On Highway Sections

The importance of simulation-based approaches for the development of automated driving functions has strongly increased in recent years. Since current validation methods probably will not be feasible for highly automated driving functions, the simulation-based techniques' relevance for the validation procedure is also increasing. In this context, it is a large challenge to simulate the huge if not infinitely large parameter space of an automated vehicle's (AV) surrounding. Therefore, this paper presents a model which allows the modeling of the statistics of traffic scenes on straight highway sections. The model is based upon recent approaches using Bayesian networks and is adapted to the usage of sum-product networks (SPNs). It is shown, that the use of SPNs can enhance the sampling speed up to 36 times compared to the Bayesian network baseline.