Current autonomous driving perception models primarily rely on supervised learning with predefined categories. However, these models struggle to detect general obstacles not included in the fixed category set due to their variability and numerous edge cases. To address this issue, we propose a combination of multimodal foundational model-based obstacle segmentation with traditional unsupervised computational geometry-based outlier detection. Our approach operates offline, allowing us to leverage non-causality, and utilizes training-free methods. This enables the detection of general obstacles in 3D without the need for expensive retraining. To overcome the limitations of publicly available obstacle detection datasets, we collected and annotated our dataset, which includes various obstacles even in distant regions.

翻译：当前自动驾驶感知模型主要依赖于预定义类别的监督学习。然而，由于通用障碍物的多样性和大量边缘情况，这些模型难以检测未包含在固定类别集中的障碍物。为解决此问题，我们提出了一种结合基于多模态基础模型的障碍物分割与基于传统无监督计算几何的离群点检测的方法。我们的方法采用离线处理，允许利用非因果性，并使用免训练技术。这使得无需昂贵的重新训练即可实现三维通用障碍物的检测。为克服公开障碍物检测数据集的局限性，我们收集并标注了自有数据集，该数据集包含了即使在远距离区域也存在的多种障碍物。