Unseen data that differ significantly from the training data can cause machine learning models to behave unpredictably, which is particularly problematic in safety-critical systems like autonomous vehicles. Detecting such data, commonly called out-of-distribution (OOD) data, is essential for ensuring the robustness of these models. Existing methods often rely on the model’s final output, which are limited since the model can be overconfident on unseen data. In this paper, we propose Routing Entropy, a novel OOD detection method that leverages the internal routing behavior of Mixture-of-Experts (MoE) models, a design increasingly adopted in modern neural networks. We hypothesize that MoE models exhibit high confidence routing for in-distribution (ID) inputs, but greater uncertainty for OOD inputs. We quantify this uncertainty by calculating the entropy of the routing scores for a given input. Experimental results on a MoE-based semantic segmentation model used for perception in autonomous driving demonstrate that Routing Entropy is effective on its own and, more importantly, provides a complementary signal to existing output-based methods. Combining Routing Entropy with an existing method significantly improves OOD detection performance. These results suggest that leveraging internal routing behavior of MoE models is a promising direction for robust OOD detection.