Autonomous train perception must identify the train’s valid path under complex railway geometry, particularly at merging and diverging switches where multiple candidate tracks coexist. Existing approaches are primarily trained as purely visual predictors and typically do not provide justification for route selection, despite the fact that valid paths depend on structured cues such as blade–stock contact, rail gaps, and track continuity. In this work, we adapt the Large Language Instructed Segmentation Assistant (LISA) to railway ego-path perception and formulate the task as reasoning-guided segmentation: given a forward-facing railway image and a natural-language query, the model predicts the valid ego-path mask and, when prompted, generates a textual explanation grounded in visible switch geometry. Our approach integrates railway-specific prompting, a tailored annotation scheme, and efficient finetuning, along with semantic segmentation supervision to support general scene understanding. Experiments on a RailSem19-based evaluation set show improved ego-path segmentation performance over the original LISA checkpoint and increased robustness to prompt variation, while qualitative results indicate that the model can produce plausible, though not always consistent, reasoning. Notably, these capabilities emerge despite the reasoning-specific dataset consisting of only 54 samples, highlighting the data efficiency of the approach. These results highlight the potential of vision-language models for more interpretable railway perception, while also underscoring the need for stronger supervision and evaluation in safety-critical settings. Code and reasoning segmentation data are available at <code>https://github.com/mvakili96/Railway_Perception_FoundationModel.</code>