Layer-Wise Modality Decomposition for Interpretable Multimodal Sensor Fusion
Jaehyun Park, Konyul Park, Daehun Kim, Junseo Park, Jun Won Choi
In autonomous driving scenarios, ensuring transparency in the decision-making process of perception model is critical, where even a single misperception can lead to life-threatening consequences. However, when data from multiple sensors are used as input, it becomes exceedingly difficult to identify the contribution of each modality to the decision. This challenge stems from the entangled representation of sensor information within the perception model. In this study, we propose Layer-Wise Modality Decomposition (LMD), a post-hoc interpretability method that disentangles modality-specific information across all layers of a pretrained fusion model. Our LMD is the first model-agnostic method to attribute the prediction of a perception model to individual input modalities in autonomous driving scenarios. We evaluate LMD on pretrained sensor fusion models in autonomous driving by applying it to both camera-radar and camera-LiDAR settings. LMD is validated through structured perturbation-based metrics and modality-wise visual decompositions, demonstrating its applicability to interpreting high-capacity multimodal architectures beyond the driving domain.