Learning Distilled Collaboration Graph for Multi-Agent Perception (2111.00643v2)

Abstract: To promote better performance-bandwidth trade-off for multi-agent perception, we propose a novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and adaptive collaboration among agents. Our key novelties lie in two aspects. First, we propose a teacher-student framework to train DiscoGraph via knowledge distillation. The teacher model employs an early collaboration with holistic-view inputs; the student model is based on intermediate collaboration with single-view inputs. Our framework trains DiscoGraph by constraining post-collaboration feature maps in the student model to match the correspondences in the teacher model. Second, we propose a matrix-valued edge weight in DiscoGraph. In such a matrix, each element reflects the inter-agent attention at a specific spatial region, allowing an agent to adaptively highlight the informative regions. During inference, we only need to use the student model named as the distilled collaboration network (DiscoNet). Attributed to the teacher-student framework, multiple agents with the shared DiscoNet could collaboratively approach the performance of a hypothetical teacher model with a holistic view. Our approach is validated on V2X-Sim 1.0, a large-scale multi-agent perception dataset that we synthesized using CARLA and SUMO co-simulation. Our quantitative and qualitative experiments in multi-agent 3D object detection show that DiscoNet could not only achieve a better performance-bandwidth trade-off than the state-of-the-art collaborative perception methods, but also bring more straightforward design rationale. Our code is available on https://github.com/ai4ce/DiscoNet.

• The paper introduces DiscoGraph, which uses a teacher-student model to distill knowledge for enhanced multi-agent perception.
• It employs innovative matrix-valued edge weights to allow agents to focus on informative regions and adapt collaboration dynamically.
• Experiments on the V2X-Sim dataset demonstrate superior detection performance and bandwidth efficiency compared to existing methods.

Overview of the Distilled Collaboration Graph for Multi-Agent Perception

The paper "Learning Distilled Collaboration Graph for Multi-Agent Perception" introduces DiscoGraph, a novel approach to optimize multi-agent perception systems by balancing performance and bandwidth requirements. The approach utilizes a teacher-student framework, emphasizing two core innovations: knowledge distillation for training the DiscoGraph and the implementation of a matrix-valued edge weight system. These innovations empower the system to perform trainable, pose-aware, and adaptive collaboration among multiple agents, which is particularly beneficial in complex tasks such as 3D object detection in autonomous driving scenarios.

Methodology and Innovations

The core methodological innovations are articulated in two dimensions:

1. Knowledge Distillation Framework: The authors describe a teacher-student model, where the teacher employs a robust early collaboration strategy with comprehensive inputs, and the student uses intermediate collaboration with restricted single-view inputs. This setup enables the student model to approximate the teacher's outputs through feature map constraints post-collaboration, enhancing both the abstraction and integration of features.
2. Matrix-Valued Edge Weights: Unlike previous approaches utilizing scalar-valued edge weights, this paper introduces matrix-valued edge weights within the Bluetooth collaboration graphs. This enables more detailed spatial attention, allowing each agent to focus on informative regions within its collaborative field. This results in the system being able to dynamically adjust to varying operational contexts, significantly improving the informative collaboration between agents.

Experimental Validation

The proposed method is validated using a newly synthesized large-scale dataset, V2X-Sim 1.0, built with CARLA and SUMO co-simulations. Experiments demonstrate that DiscoNet achieves a superior performance-bandwidth trade-off when compared to existing collaborative perception methods.

Key numerical results highlight the efficacy of the approach:

• DiscoNet achieves remarkable improvements in average precision (AP) over existing methods, particularly V2VNet and When2com.
• Even with significant data compression, DiscoNet still outperforms other systems, indicating robust communication efficiency.

Implications and Future Directions

DiscoGraph's unique approach to adaptive, trainable collaboration introduces vital implications for both theoretical research and practical applications in AI.

• Practical Implications: In practical terms, the refined communication and processing efficiency enable more reliable and responsive multi-agent systems. The advancements in spatial awareness through matrix-valued edge weights may translate to improved real-time applications in autonomous driving and robotics.
• Theoretical Implications: This work extends the conceptual framework of knowledge distillation beyond traditional neural network efficiency to collaborative feature learning in distributed scenarios. It also invites further exploration into matrix-valued attention mechanisms within other graph-based systems.

Future research could explore further optimization of the edge encoder within the DiscoGraph, potentially leveraging additional contextual information like environmental dynamics. Investigating the integration of this framework into broader autonomous systems or extending its application to different multi-agent paradigms could also prove fruitful avenues for exploration.

Concluding Remarks

By introducing DiscoGraph, the authors have contributed a significant step toward enhancing multi-agent perception, both in efficacy and efficiency. This well-structured, innovative approach not only serves as a comprehensive solution to the performance-bandwidth optimization challenge but also lays groundwork for ongoing research into adaptive, collaboration-centric AI systems.