DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits

• URL: http://arxiv.org/abs/2509.24903v1
• Date: Mon, 29 Sep 2025 15:13:03 GMT
• Title: DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits
• Authors: Lantao Li, Kang Yang, Rui Song, Chen Sun,
• Abstract summary: Diffusion on Reinforced Cooperative Perception (DRCP) is a real-time deployable framework designed to address issues in dynamic driving environments.<n>The proposed system achieves real-time performance on mobile platforms while significantly improving robustness under challenging conditions.
• Score: 11.34052678290095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperative perception enabled by Vehicle-to-Everything communication has shown great promise in enhancing situational awareness for autonomous vehicles and other mobile robotic platforms. Despite recent advances in perception backbones and multi-agent fusion, real-world deployments remain challenged by hard detection cases, exemplified by partial detections and noise accumulation which limit downstream detection accuracy. This work presents Diffusion on Reinforced Cooperative Perception (DRCP), a real-time deployable framework designed to address aforementioned issues in dynamic driving environments. DRCP integrates two key components: (1) Precise-Pyramid-Cross-Modality-Cross-Agent, a cross-modal cooperative perception module that leverages camera-intrinsic-aware angular partitioning for attention-based fusion and adaptive convolution to better exploit external features; and (2) Mask-Diffusion-Mask-Aggregation, a novel lightweight diffusion-based refinement module that encourages robustness against feature perturbations and aligns bird's-eye-view features closer to the task-optimal manifold. The proposed system achieves real-time performance on mobile platforms while significantly improving robustness under challenging conditions. Code will be released in late 2025.

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