RMP: Robust Multi-Modal Perception Under Missing Condition

Multi-modal perception is a core technology for edge devices to achieve safe and reliable environmental understanding in autonomous driving scenarios. In recent years, most approaches have focused on integrating complementary signals from diverse sensors, including cameras and LiDAR, to improve scene understanding in complex traffic environments, thereby attracting significant attention. However, in real-world applications, sensor failures frequently occur; for instance, cameras may malfunction in scenarios with poor illumination, which severely reduces the accuracy of perception models. To overcome this issue, we propose a robust multi-modal perception pipeline designed to improve model performance under missing modality conditions. Specifically, we design a missing feature reconstruction mechanism to reconstruct absent features by leveraging intra-modal common clues. Furthermore, we introduce a multi-modal adaptive fusion strategy to facilitate adaptive multi-modal integration through inter-modal feature interactions. Extensive experiments on the nuScenes benchmark demonstrate that our method achieves SOTA-level performance under missing-modality conditions.