Cooperative perception is essential to enhance the efficiency and safety of future transportation systems, requiring extensive data sharing among vehicles on the road, which raises significant privacy concerns. Federated learning offers a promising solution by enabling data privacy-preserving collaborative enhancements in perception, decision-making, and planning among connected and autonomous vehicles (CAVs). However, federated learning is impeded by significant challenges arising from data heterogeneity across diverse clients, potentially diminishing model accuracy and prolonging convergence periods. This study introduces a specialized federated learning framework for CP, termed the federated dynamic weighted aggregation (FedDWA) algorithm, facilitated by dynamic adjusting loss (DALoss) function. This framework employs dynamic client weighting to direct model convergence and integrates a novel loss function that utilizes Kullback-Leibler divergence (KLD) to counteract the detrimental effects of non-independently and identically distributed (Non-IID) and unbalanced data. Utilizing the BEV transformer as the primary model, our rigorous testing on the OpenV2V dataset, augmented with FedBEVT data, demonstrates significant improvements in the average intersection over union (IoU). These results highlight the substantial potential of our federated learning framework to address data heterogeneity challenges in CP, thereby enhancing the accuracy of environmental perception models and facilitating more robust and efficient collaborative learning solutions in the transportation sector.

翻译：协同感知对于提升未来交通系统的效率与安全性至关重要，其要求道路车辆间进行广泛数据共享，这引发了显著的隐私问题。联邦学习通过支持网联自动驾驶车辆在感知、决策与规划环节实现隐私保护的协作增强，提供了一种有前景的解决方案。然而，联邦学习面临不同客户端间数据异质性带来的重大挑战，这会降低模型精度并延长收敛周期。本研究提出了一种面向协同感知的专用联邦学习框架，即联邦动态加权聚合算法，该算法通过动态调节损失函数实现优化。该框架采用动态客户端权重引导模型收敛，并整合了一种利用Kullback-Leibler散度的新型损失函数，以抵消非独立同分布数据与不平衡数据产生的不利影响。以BEV Transformer作为主模型，我们在经FedBEVT数据增强的OpenV2V数据集上的严格测试表明，平均交并比实现了显著提升。这些结果凸显了我们的联邦学习框架在解决协同感知中数据异质性挑战方面的巨大潜力，从而提升环境感知模型的精度，并为交通领域提供更鲁棒高效的协作学习解决方案。