The field of autonomous driving and computer vision is moving towards improving the robustness and reliability of perception models in real-world scenarios. Recent research has focused on addressing the challenges of out-of-distribution (OOD) detection and segmentation, which is crucial for safety-critical applications. The use of unsupervised domain adaptation (UDA) and vision foundation models (VFMs) has shown promising results in improving generalization performance. However, the effectiveness of these methods in more realistic and diverse data scenarios is still being explored. Noteworthy papers in this area include:

• The proposal of Dream-Box, a method that generates object-wise outliers in pixel space for OOD detection, providing concrete visualization of generated OOD objects.
• The introduction of Segmenting Objectiveness and Task-Awareness (SOTA), a novel framework that enhances the segmentation of objectiveness and filters anomalies irrelevant to road navigation tasks.
• The development of a challenging benchmark, ComsAmy, for open-set anomaly segmentation in complex scenarios, and a novel energy-entropy learning strategy that integrates complementary information to bolster the robustness of anomaly segmentation.