Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles. However, the absence of feature maps shared from other vehicles can lead to a significant decline in object detection performance for cooperative perception models compared to standalone 3D detection models. This drawback impedes the adoption of cooperative perception as vehicle resources are often insufficient to concurrently employ two perception models. To tackle this issue, we present Simultaneous Individual and Cooperative Perception (SiCP), a generic framework that supports a wide range of the state-of-the-art standalone perception backbones and enhances them with a novel Dual-Perception Network (DP-Net) designed to facilitate both individual and cooperative perception. In addition to its lightweight nature with only 0.13M parameters, DP-Net is robust and retains crucial gradient information during feature map fusion. As demonstrated in a comprehensive evaluation on the OPV2V dataset, thanks to DP-Net, SiCP surpasses state-of-the-art cooperative perception solutions while preserving the performance of standalone perception solutions.

翻译：传统上，网联自动驾驶车辆的协同感知通过融合两辆及以上车辆的特征图实现。然而，当缺乏其他车辆共享的特征图时，协同感知模型的物体检测性能相较于独立3D检测模型会出现显著下降。这一缺陷阻碍了协同感知的实际应用，因为车辆计算资源通常不足以同时运行两个感知模型。为解决该问题，我们提出了同步个体与协同感知（SiCP）通用框架，该框架支持多种当前最先进的独立感知主干网络，并通过新型双感知网络（DP-Net）增强其同时实现个体感知与协同感知的能力。DP-Net不仅参数轻量（仅0.13M参数），且具有鲁棒性，在特征图融合过程中能保留关键梯度信息。基于OPV2V数据集的全面评估表明，借助DP-Net，SiCP在保持独立感知方案性能的同时，超越了当前最先进的协同感知解决方案。