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GT-Space: Enhancing Heterogeneous Collaborative Perception with Ground Truth Feature Space

arXiv:2603.19308v1 Announce Type: new Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies in handling {\em heterogeneous} features from agents equipped with different sensing modalities or model architectures, which complicates data fusion. Existing approaches often require retraining encoders or designing interpreter modules for pairwise feature alignment, but these solutions are not scalable in practice. To address this, we propose {\em GT-Space}, a flexible and scalable collaborative perception framework for heterogeneous agents. GT-Space constructs a common feature space from ground-truth labels, providing a unified reference for feature alignment. With this shared space, agents only need a single adapter module to project their features, eliminating the need for pairwise interactions with other agents. Furthermore, we design a fusion network trained with contrastive l

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Wentao Wang, Haoran Xu, Guang Tan · · 1 min read · 21 views
#cs.LG #cs.AI #cs.MA

arXiv:2603.19308v1 Announce Type: new Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies in handling {\em heterogeneous} features from agents equipped with different sensing modalities or model architectures, which complicates data fusion. Existing approaches often require retraining encoders or designing interpreter modules for pairwise feature alignment, but these solutions are not scalable in practice. To address this, we propose {\em GT-Space}, a flexible and scalable collaborative perception framework for heterogeneous agents. GT-Space constructs a common feature space from ground-truth labels, providing a unified reference for feature alignment. With this shared space, agents only need a single adapter module to project their features, eliminating the need for pairwise interactions with other agents. Furthermore, we design a fusion network trained with contrastive losses across diverse modality combinations. Extensive experiments on simulation datasets (OPV2V and V2XSet) and a real-world dataset (RCooper) demonstrate that GT-Space consistently outperforms baselines in detection accuracy while delivering robust performance. Our code will be released at https://github.com/KingScar/GT-Space.

Executive Summary

The article proposes GT-Space, a novel framework for enhancing heterogeneous collaborative perception in autonomous driving. By leveraging ground-truth labels, GT-Space constructs a unified feature space for various agents, eliminating the need for pairwise feature alignment and retraining encoders. The framework's fusion network is trained with contrastive losses across diverse modality combinations, achieving robust performance in detection accuracy. Extensive experiments on simulation and real-world datasets demonstrate GT-Space's superiority over existing approaches. The proposed framework has significant implications for the development of robust and scalable collaborative perception systems in autonomous driving applications. The authors' decision to release their code on GitHub will facilitate further research and adoption of the GT-Space framework.

Key Points

  • ▸ GT-Space constructs a common feature space from ground-truth labels for heterogeneous agents.
  • ▸ The framework eliminates the need for pairwise feature alignment and retraining encoders.
  • ▸ A fusion network trained with contrastive losses achieves robust performance in detection accuracy.

Merits

Strength in Scalability

GT-Space's ability to handle heterogeneous features from multiple agents without requiring pairwise interactions or retraining encoders makes it a scalable solution for collaborative perception.

Robust Performance

The fusion network trained with contrastive losses demonstrates robust performance in detection accuracy, making GT-Space a reliable choice for autonomous driving applications.

Demerits

Assumes Access to Ground-Truth Labels

GT-Space relies on ground-truth labels to construct the unified feature space, which may not be available in all scenarios, limiting its applicability.

May Require Significant Computational Resources

The fusion network and contrastive losses may require significant computational resources, which could be a limitation in resource-constrained environments.

Expert Commentary

The proposed GT-Space framework demonstrates a significant advancement in the field of collaborative perception for autonomous driving. The authors' innovative approach to leveraging ground-truth labels and contrastive losses achieves robust performance in detection accuracy, making it a compelling solution for industry and academia. However, the framework's reliance on ground-truth labels and potential computational resource requirements are notable limitations that require further investigation. Overall, the GT-Space framework has the potential to revolutionize the development of autonomous driving systems and deserves further attention and research.

Recommendations

  • ✓ Further research should focus on developing methods to obtain ground-truth labels in scenarios where they are not readily available.
  • ✓ Investigating the application of GT-Space to other domains beyond autonomous driving, such as robotics and computer vision, could lead to broader adoption and innovation.

Sources

Original: arXiv - cs.LG

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