Collaborative Perception

Collaborative perception (CP) aims to enhance individual agents' environmental understanding by sharing and integrating sensor data. Current research focuses on improving CP efficiency through techniques like diffusion models for data compression, direction-aware attention mechanisms for targeted perception, and spatial-temporal state space models for efficient feature representation and fusion. These advancements address challenges in communication bandwidth, computational resources, and robustness to localization errors, impacting applications such as autonomous driving and multi-robot systems by improving perception accuracy and reliability. The field is also actively exploring solutions for heterogeneous sensor integration and robustness to adversarial attacks.