Pedestrian Detection (PD) is one the most studied application areas of object detection. Although an important effort is already given to develop advanced classifiers for self-driving cars (SDC) especially in recent years, studies about onboard railway systems remain very limited. Although the similarity of railway and highway scenarios suggests that systems developed for SDC-PD can be used for rail vehicles, it is also necessary to examine the low level of generalization ability and the high degree of dependence of the PD test performance on the training set. In this paper, the feasibility of PD systems, whose effectiveness on SDC is proved, was examined on railway systems. For this purpose, SDC-PD systems, created with different training benchmarks such as Caltech and INRIA, have been applied to railway system scenes with various difficulties and the results have been examined. The findings show that the direct implementation of classifiers for SDC does not provide sufficient success to come up with specific challenges to railway systems.