Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps (2209.12836v1)

Abstract: Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate. Based on this novel spatial confidence map, we propose Where2comm, a communication-efficient collaborative perception framework. Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication. To evaluate Where2comm, we consider 3D object detection in both real-world and simulation scenarios with two modalities (camera/LiDAR) and two agent types (cars/drones) on four datasets: OPV2V, V2X-Sim, DAIR-V2X, and our original CoPerception-UAVs. Where2comm consistently outperforms previous methods; for example, it achieves more than $100,000 \times$ lower communication volume and still outperforms DiscoNet and V2X-ViT on OPV2V. Our code is available at https://github.com/MediaBrain-SJTU/where2comm.

• The paper introduces a framework that uses spatial confidence maps to guide efficient, selective communication in multi-agent perception systems.
• It employs targeted message fusion and dynamic graph construction to optimize bandwidth usage while maintaining high 3D object detection accuracy.
• Empirical results on multiple datasets demonstrate a dramatic reduction in communication volume and improved accuracy across diverse sensor modalities.

An Essay on "Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps"

The paper "Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps" introduces a sophisticated framework aimed at enhancing the efficiency of communication in multi-agent collaborative perception systems. This framework, termed "Where2comm," addresses the salient trade-off between perception capability and communication bandwidth, an area that has seen increasing importance given the expanding application of multi-agent systems across various domains.

The central innovation presented in this paper is the application of spatial confidence maps, which guide agents in selectively sharing spatially sparse, yet perceptually critical information. The spatial confidence map is effectively utilized to determine "where" communication should occur, focusing bandwidth on areas with high perceptual significance. This approach deviates from traditional methods where agents are typically obligated to share information from all spatial areas equally, which can result in significant bandwidth inefficiencies.

Overview of Where2comm

Where2comm is designed with a robust architecture consisting of several key modules. These include:

1. Spatial Confidence Generator: This module generates confidence maps that indicate perceptually critical areas. The generation relies on leveraging detection confidence maps, assuming areas with higher detection certainty contribute more valuable perceptive information.
2. Spatial Confidence-Aware Communication: This module is pivotal to the framework, adapting message contents based on spatial importance. The message includes a request map and a spatially selective feature map, thus targeting communication efforts only where it is needed. This approach effectively reduces unnecessary communication volume without compromising the integrity of sensory perception.
3. Communication Graph Construction: The framework intelligently constructs communication graphs based on necessity determined by the spatial confidence and request maps, transitioning from traditionally fully connected graphs to more efficient, sparsely connected structures.
4. Spatial Confidence-Aware Message Fusion: Utilizing a well-designed attention mechanism, this module fuses messages by considering spatial confidence inputs and sensor positional encoding, offering a more comprehensive aggregation of perceptual data across agents.

These innovations allow Where2comm to handle challenges in varying communication conditions by dynamically adjusting spatial communication areas and optimizing the performance-bandwidth trade-off.

Empirical Evaluation

The 3D object detection tasks conducted on datasets such as OPV2V, V2X-Sim, DAIR-V2X, and the newly introduced CoPerception-UAVs demonstrate the framework's efficacy. Where2comm significantly outperformed existing methods such as DiscoNet and V2X-ViT, demonstrating over $100,000 \times$ reduction in communication volume on certain tasks while maintaining superior detection performance. The adaptability of Where2comm to diverse modalities (camera/LiDAR) and agent types (cars/drones) further highlights its robustness and application versatility.

Implications and Future Directions

From a theoretical standpoint, this paper introduces novel insights into spatial feature prioritization in multi-agent perception, advancing understanding in attention-based communications. Practically, Where2comm offers substantial improvements in collaborative perception systems, making it a valuable addition to real-world applications like autonomous driving and UAV swarms.

Looking forward, further developments could extend this spatially focused communication to the temporal domain, addressing the "when" aspect of communication, which remains an open challenge. Pragmatic compression strategies and emergent communication protocols also present exciting avenues for continued research that might further enhance system efficiency.

In summary, "Where2comm" stands out as a robust and innovative approach to enhancing collaborative perception in multi-agent systems. The clear empirical benefits, combined with a solid theoretical underpinning, ensure its relevance to both academic research and practical implementations. The focus on communication efficiency while maintaining perceptual accuracy marks a significant step forward in the broader field of multi-agent systems and artificial intelligence.