An Extensible Framework for Open Heterogeneous Collaborative Perception (2401.13964v3)

Abstract: Collaborative perception aims to mitigate the limitations of single-agent perception, such as occlusions, by facilitating data exchange among multiple agents. However, most current works consider a homogeneous scenario where all agents use identity sensors and perception models. In reality, heterogeneous agent types may continually emerge and inevitably face a domain gap when collaborating with existing agents. In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost? To address this problem, we propose HEterogeneous ALliance (HEAL), a novel extensible collaborative perception framework. HEAL first establishes a unified feature space with initial agents via a novel multi-scale foreground-aware Pyramid Fusion network. When heterogeneous new agents emerge with previously unseen modalities or models, we align them to the established unified space with an innovative backward alignment. This step only involves individual training on the new agent type, thus presenting extremely low training costs and high extensibility. To enrich agents' data heterogeneity, we bring OPV2V-H, a new large-scale dataset with more diverse sensor types. Extensive experiments on OPV2V-H and DAIR-V2X datasets show that HEAL surpasses SOTA methods in performance while reducing the training parameters by 91.5% when integrating 3 new agent types. We further implement a comprehensive codebase at: https://github.com/yifanlu0227/HEAL

• The paper presents HEAL, a novel framework that enables the integration of new heterogeneous sensor agents using a backward alignment mechanism.
• The paper employs a multi-scale Pyramid Fusion network to unify feature representations and significantly reduce training costs by over 90% for new agents.
• The paper validates HEAL on diverse datasets, demonstrating superior collaborative detection performance compared to state-of-the-art methods.

An Extensible Framework for Open Heterogeneous Collaborative Perception

The paper presents a novel approach to collaborative perception, addressing the gap that arises in heterogeneous environments where multiple agents with distinct sensor modalities and perception models come together. The work introduces the HEterogeneous ALliance (HEAL), an innovative and extensible framework designed for open heterogeneous collaborative perception. It is built to integrate new agent types into an existing collaborative perception framework efficiently, ensuring minimal training cost and maintaining high perception performance.

Overview and Problem Definition

The traditional focus in collaborative perception has been on homogeneous settings, which assume identical sensor types and models across all agents. This assumption simplifies system design but limits the applicability of these systems to real-world scenarios where agent heterogeneity is the norm. New agents with diverse and previously unseen sensor modalities or models may continuously emerge, demanding a solution that can readily integrate these new types into existing collaborative frameworks. The paper addresses this necessity with the HEAL framework.

HEAL Framework

Collaboration Base Training:

HEAL initializes with a collaboration base of homogeneous agents. During this phase, the framework establishes a unified feature space that all initial agents can contribute to using a multi-scale and foreground-aware Pyramid Fusion network. This unified feature space serves as the foundation for integrating future heterogeneous agents entering the cooperative environment.

New Agent Type Training:

Once the unified feature space is established, the framework supports the integration of new agent types using a novel backward alignment mechanism. This involves training each new agent type's encoder individually to align its feature representation with the pre-established unified feature space. This alignment is both computationally inexpensive and conserves memory since only the encoder of the new agent type undergoes training while leveraging the existing, fixed Pyramid Fusion network as the detection back-end.

Evaluation and Results

HEAL's efficacy is validated through comprehensive evaluations on the newly proposed OPV2V-H dataset, which enriches the standard OPV2V dataset with more diverse sensor types, and on the DAIR-V2X dataset. The empirical results show that HEAL significantly outperforms state-of-the-art (SOTA) methods in collaborative detection metrics, reducing the number of training parameters by 91.5% when adding three new heterogeneous agent types.

Implications and Future Work

The introduction of HEAL represents a substantial advancement in the field of collaborative perception, particularly in heterogeneous settings. The framework's ability to accommodate new agent types with minimal training costs advocates for practical real-world deployment, addressing privacy concerns by enabling local training on new agents. Furthermore, the availability of the OPV2V-H dataset promotes further research and development in heterogeneous collaborative perception systems.

Future developments could explore the incorporation of dynamic training and adaptation methods for HEAL, enabling real-time learning and integration in dynamic environments. Additionally, examining the robustness of HEAL in scenarios with extreme variability in sensor modalities and perception capability will be a pertinent extension.

Overall, HEAL significantly enhances the adaptability and extensibility of collaborative perception systems, marking a pivotal step forward in multi-agent perception frameworks in robotics and autonomous systems.