INSTINCT: Instance-Level Interaction Architecture for Query-Based Collaborative Perception
This work addresses bandwidth limitations in multi-agent perception for autonomous vehicles, offering an incremental improvement over existing query-based methods.
The paper tackles the problem of bandwidth constraints in collaborative perception systems for autonomous vehicles by proposing INSTINCT, a query-based instance-level interaction architecture, which improves accuracy by 13.23% on DAIR-V2X and 33.08% on V2V4Real datasets while reducing communication bandwidth to 1/281 and 1/264 compared to state-of-the-art methods.
Collaborative perception systems overcome single-vehicle limitations in long-range detection and occlusion scenarios by integrating multi-agent sensory data, improving accuracy and safety. However, frequent cooperative interactions and real-time requirements impose stringent bandwidth constraints. Previous works proves that query-based instance-level interaction reduces bandwidth demands and manual priors, however, LiDAR-focused implementations in collaborative perception remain underdeveloped, with performance still trailing state-of-the-art approaches. To bridge this gap, we propose INSTINCT (INSTance-level INteraCtion ArchiTecture), a novel collaborative perception framework featuring three core components: 1) a quality-aware filtering mechanism for high-quality instance feature selection; 2) a dual-branch detection routing scheme to decouple collaboration-irrelevant and collaboration-relevant instances; and 3) a Cross Agent Local Instance Fusion module to aggregate local hybrid instance features. Additionally, we enhance the ground truth (GT) sampling technique to facilitate training with diverse hybrid instance features. Extensive experiments across multiple datasets demonstrate that INSTINCT achieves superior performance. Specifically, our method achieves an improvement in accuracy 13.23%/33.08% in DAIR-V2X and V2V4Real while reducing the communication bandwidth to 1/281 and 1/264 compared to state-of-the-art methods. The code is available at https://github.com/CrazyShout/INSTINCT.