Reason-to-Transmit: Deliberative Adaptive Communication for Cooperative Perception

Abstract

Cooperative perception among autonomous agents overcomes the limitations of single-agent sensing, but bandwidth constraints in vehicle-to-everything (V2X) networks require efficient communication policies. Existing approaches rely on reactive mechanisms, such as confidence maps, learned gating, or sparse masks, to decide what to transmit, without reasoning about why a message benefits the receiver. We introduce Reason-to-Transmit (R2T), a framework that equips each agent with a lightweight transformer-based module that reasons over local scene context, estimated neighbor information gaps, and bandwidth budget to make per-region transmission decisions. Trained end-to-end with a bandwidth-aware objective, R2T is evaluated against nine baselines in a multi-agent bird's-eye-view perception environment. Any communication improves performance by about 58% AP over no communication. At low bandwidth, all selective methods perform similarly, but R2T shows clear gains under high occlusion, where information asymmetry is greatest, approaching oracle performance. All methods degrade gracefully under packet drops up to 50%, showing robustness to communication failures. These results indicate that while fusion design dominates performance, deliberative communication provides additional gains in challenging scenarios. R2T introduces a reasoning-based approach to communication, enabling more efficient and context-aware information sharing in cooperative perception.

Figures (4)

• Figure 1: Overview of the Reason-to-Transmit (R2T) framework. Each agent encodes local observations into a BEV feature map, generates spatial region candidates, and applies a reasoning-based transmit policy that jointly considers local features, neighbor state estimates, and the bandwidth budget. Selected regions are transmitted to neighbors and fused via gated cross-attention before final detection.
• Figure 2: Communication decision pipeline. The reasoning module attends over region tokens augmented with agent context, neighbor state estimates, and a budget token, producing per-region transmit probabilities under the bandwidth constraint.
• Figure 3: AP vs. bandwidth budget. All selective methods dramatically outperform No Communication. Error bands show $\pm$1 std over 5 seeds. The secondary axis shows bandwidth in KB.
• Figure 4: Qualitative visualization of communication decisions. R2T selectively transmits regions containing occluded objects that the receiver cannot observe, while suppressing regions with redundant information.