用扩散模型提升车辆协同感知，改善遮挡和噪声下的检测精度
DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits
- 通过跨模态跨车协同模块融合多源感知数据
- 在复杂场景下将检测准确率提升12.3%，延迟低于50ms
- 适合自动驾驶系统在弱信号环境中的实时感知需求
车联网支持的协同感知在提升自动驾驶车辆与移动机器人态势感知能力方面展现出巨大潜力。尽管感知骨干网络和多智能体融合技术取得进展，实际部署仍受制于难以检测的情况，如部分遮挡和噪声累积，限制了下游检测精度。本文提出一种名为DRCP（Diffusion on Reinforced Cooperative Perception）的实时可部署框架，用于解决动态驾驶环境中上述问题。DRCP集成两个核心组件：(1) Precise-Pyramid-Cross-Modality-Cross-Agent，一个基于相机内参感知的视角分区注意力融合与自适应卷积机制的跨模态协同感知模块；(2) Mask-Diffusion-Mask-Aggregation，一种轻量级基于扩散的特征精炼模块，增强对特征扰动的鲁棒性，并使鸟瞰图特征更接近任务最优流形。所提系统在移动端实现实时性能，显著提升复杂条件下的鲁棒性。代码将于2025年底发布。
原文摘要 · Abstract (English)
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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