OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport
This addresses a critical issue for autonomous driving systems in unstructured environments, offering a method with strong OOD generalization using limited data, though it is incremental as it builds on existing multi-modal fusion approaches.
The paper tackles the problem of degraded traversable area segmentation in out-of-distribution off-road scenarios for autonomous driving, proposing OT-Drive, which achieves 95.16% mIoU on ORFD OOD scenarios, outperforming prior methods by 6.35%.
Reliable traversable area segmentation in unstructured environments is critical for planning and decision-making in autonomous driving. However, existing data-driven approaches often suffer from degraded segmentation performance in out-of-distribution (OOD) scenarios, consequently impairing downstream driving tasks. To address this issue, we propose OT-Drive, an Optimal Transport--driven multi-modal fusion framework. The proposed method formulates RGB and surface normal fusion as a distribution transport problem. Specifically, we design a novel Scene Anchor Generator (SAG) to decompose scene information into the joint distribution of weather, time-of-day, and road type, thereby constructing semantic anchors that can generalize to unseen scenarios. Subsequently, we design an innovative Optimal Transport-based multi-modal fusion module (OT Fusion) to transport RGB and surface normal features onto the manifold defined by the semantic anchors, enabling robust traversable area segmentation under OOD scenarios. Experimental results demonstrate that our method achieves 95.16% mIoU on ORFD OOD scenarios, outperforming prior methods by 6.35%, and 89.79% mIoU on cross-dataset transfer tasks, surpassing baselines by 13.99%.These results indicate that the proposed model can attain strong OOD generalization with only limited training data, substantially enhancing its practicality and efficiency for real-world deployment.