发表机构
University of California, Irvine（加利福尼亚大学欧文分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有不确定性感知融合方法泛化性差的问题，提出CRUISE框架，结合VLM引导的UQ模块与动态自适应机制，实现鲁棒的自动驾驶跨模态传感器融合。
AI 中文摘要
现代自动驾驶汽车配备了相机、激光雷达（LiDAR）和雷达等多种传感器，用于实现全面的环境感知。然而，跨模态特征的鲁棒融合仍是一项关键挑战，因为不同传感器的可靠性在各类真实驾驶场景（包括能见度差和恶劣天气）中存在显著差异。虽然不确定性量化（UQ）可通过让模型优先选择可靠信号缓解该问题，但现有不确定性感知融合方法通常依赖简单的特征级不确定性估计，因此在复杂的分布外场景中往往无法有效泛化。为解决这一局限，我们提出了CRUISE，一种新型不确定性感知跨模态传感器融合框架。CRUISE集成了视觉语言模型（VLM）引导的UQ模块，可生成细粒度的像素级不确定性估计；通过利用VLM丰富的先验知识和出色的上下文推理能力，我们的方法为融合过程提供了高信息量的引导。此外，我们引入了动态自适应机制，明确建模并捕获跨模态依赖关系，确保框架充分利用多传感器输入固有的互补特性。
英文摘要
Modern autonomous vehicles are equipped with multiple sensors, such as cameras, LiDAR, and radar, for comprehensive environmental perception. However, robust cross-modal feature fusion remains a critical challenge, as the reliability of each sensor varies significantly across diverse real-world driving conditions, including poor visibility and adverse weather. While uncertainty quantification (UQ) mitigates this issue by allowing models to prioritize reliable signals, existing uncertainty-aware fusion methods typically rely on simple feature-level uncertainty estimates and thus often fail to generalize effectively in complex, out-of-distribution scenarios. To address this limitation, we propose CRUISE, a novel uncertainty-aware cross-modal sensor fusion framework. CRUISE integrates a vision-language model (VLM)-guided UQ module that generates fine-grained, pixel-level uncertainty estimates. By leveraging the VLM's rich prior knowledge and superior contextual reasoning, our approach provides a highly informative guide for the fusion process. Furthermore, we introduce a dynamic adaptive mechanism that explicitly models and captures cross-modal dependencies, ensuring the framework fully exploits the inherent complementary nature of multi-sensor inputs.
