Communication-Efficient Multi-Task Collaborative Perception Using Joint Representation Learning

Collaborative perception (CP) improves the environmental understanding of connected agents via the exchange of visual scene representations. While prior works focus on single-task perception, autonomous agents often need to run multiple tasks concurrently, for comprehensive scene understanding. A direct application of existing CP frameworks requires each agent to send a separate feature representation for every task, causing communication to scale poorly with both the number of tasks and agents. We study multi-task CP from a joint representation-sharing perspective. Intuitively, task-specific features produced by an agent are distinct encodings of the same local observation and hence, contain overlapping scene information. We formalize this using rate-distortion theory, comparing separate task-wise coding with joint coding of multiple task-specific feature representations. The analysis shows that, for the same task-level distortion requirements, jointly coding multiple task representations can reduce communication by leveraging the information shared among them. Motivated by this insight, we develop ORAT (One Representation for All Tasks), a multi-task CP framework that maps multiple task-specific representations into a single base representation before transmission. Unlike prior task-wise communication methods that apply compression, task-relevant region selection, or receiver-driven refinement on each task-specific representation separately, ORAT applies these operations directly on that base. The receiver agent adapts the shared base representation into task-compatible features through learned task-specific projection modules, and fuses them with its local features for downstream prediction. Experiments on OPV2V and V2X-Sim datasets show that ORAT preserves perception performance for all tasks while reducing communication by factors ranging from 3.61× to 4810× compared to prior task-specific transmission frameworks.