Interaction modeling plays a huge role in understanding human behavior in traffic. This is especially relevant when it comes to interactions between vehicles and vulnerable road users such as pedestrians. Thus, pedestrian intention prediction is an ongoing field of research in order to understand the pedestrians' decision making. Most state-of-the-art prediction frameworks are trained on large-scale datasets and evaluated with respect to acknowledged benchmarks. These datasets lack the ability to account for the reciprocal nature of interactions between pedestrians and vehicles and the effects of the two agents influencing each other. In this work, we demonstrate first steps towards assessing pedestrian prediction algorithms within realistic scenarios including the interaction effects arising from its interplay with a planning component. For this, we validate an existing prediction framework trained on benchmark datasets with situations from a virtual reality (VR) pedestrian-vehicle co-simulator that allows us to include the effect of vehicle planning on pedestrian behavior. We evaluate the performance of the prediction framework comparing data from pre-recorded real-world datasets with data from our co-simulation study and conduct an ablation analysis to identify the most important features for pedestrian intention prediction. The results highlight the significance of pedestrian action and proximity to the road.