Abstract
While automated vehicles hold the potential to significantly reduce traffic accidents, their perception systems remain vulnerable to sensor degradation caused by adverse weather and environmental occlusions. Collective perception, which enables vehicles to share information, offers a promising approach to overcoming these limitations. However, to this date collective perception in adverse weather is mostly unstudied. Therefore, we conduct the first study of LiDAR-based collective perception under diverse weather conditions and present a novel multi-task architecture for LiDAR-based collective perception under adverse weather. Adverse weather conditions can not only degrade perception capabilities, but also negatively affect bandwidth requirements and latency due to the introduced noise that is also transmitted and processed. Denoising prior to communication can effectively mitigate these issues. Therefore, we propose DenoiseCP-Net, a novel multi-task architecture for LiDAR-based collective perception under adverse weather conditions. DenoiseCP-Net integrates voxel-level noise filtering and object detection into a unified sparse convolution backbone, eliminating redundant computations associated with two-stage pipelines. This design not only reduces inference latency and computational cost but also minimizes communication overhead by removing non-informative noise. We extended the well-known OPV2V dataset by simulating rain, snow, and fog using our realistic weather simulation models. We demonstrate that DenoiseCP-Net achieves near-perfect denoising accuracy in adverse weather, reduces the bandwidth requirements by up to 23.6% while maintaining the same detection accuracy and reducing the inference latency for cooperative vehicles.
Abstract (translated)
尽管自动驾驶车辆有望大幅减少交通事故，但其感知系统仍然容易受到恶劣天气和环境遮挡导致的传感器退化的影响。集体感知（即车辆间的信息共享）为克服这些限制提供了一种有前景的方法。然而，迄今为止，在恶劣天气条件下进行集体感知的研究仍很少。因此，我们进行了首个基于LiDAR的集体感知在各种天气条件下的研究，并提出了一个新颖的任务多合一架构——用于恶劣天气下基于LiDAR的集体感知。 恶劣天气不仅会降低感知能力，还会由于引入的噪声而增加带宽需求和延迟，这些噪声也会被传输和处理。因此，在通信前进行去噪可以有效缓解这些问题。为此，我们提出了一种新颖的任务多合一架构——DenoiseCP-Net，用于在恶劣天气条件下基于LiDAR的集体感知。DenoiseCP-Net集成了体素级噪声过滤与目标检测到一个统一的稀疏卷积骨干网络中，消除了两阶段流水线相关的冗余计算。这种设计不仅减少了推理延迟和计算成本，还通过去除非信息性噪声来最小化通信开销。 为了研究DenoiseCP-Net的表现，我们扩展了著名的OPV2V数据集，并使用我们的现实天气模拟模型对其进行了雨、雪和雾的仿真。我们展示了在恶劣天气条件下，DenoiseCP-Net能够实现近乎完美的去噪精度，在保持相同检测准确性的前提下减少带宽需求高达23.6%，并降低了协同车辆的推理延迟。
URL
https://arxiv.org/abs/2507.06976
