Enhancing collaborative perception through multi-scale contextual information integration

In autonomous driving, perception systems face challenges from dynamic environments such as occlusions, changing lighting, and unpredictable traffic. These conditions make it hard to capture fine local details and broad context, while real-time operation demands high efficiency and low communication cost. In this paper, we propose a multi-scale contextual information integration (MSCI) framework designed to enhance collaborative perception. The method employs multi-scale adaptive attention augmentation to focus on relevant features across spatial scales, capturing fine-grained details and wider contextual cues to improve perception accuracy. The context-aware perception enhancement module then combines local and global information. It refines features to keep perception robust and stable in changing or challenging environments. Finally, the GRU-based dynamic booster embeds a motion-aware mechanism into the recurrent unit. This strengthens temporal modeling for sequential data and improves real-time decision making. Experimental results demonstrate that the proposed method achieves notable improvements in both detection accuracy and communication efficiency.