Deep learning is the method of choice for trajectory prediction for autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires the availability of sufficiently rich and high-quality centralized datasets, which easily leads to privacy leakage. Besides, uncertainty-awareness becomes increasingly important for safety-crucial cyber physical systems whose prediction module heavily relies on machine learning tools. In this paper, we relax the data collection requirement and enhance uncertainty-awareness by using Federated Learning on Connected Autonomous Vehicles with an uncertainty-aware global objective. We name our algorithm as FLTP. We further introduce ALFLTP which boosts FLTP via using active learning techniques in adaptatively selecting participating clients. We consider both negative log-likelihood (NLL) and aleatoric uncertainty (AU) as client selection metrics. Experiments on Argoverse dataset show that FLTP significantly outperforms the model trained on local data. In addition, ALFLTP-AU converges faster in training regression loss and performs better in terms of NLL, minADE and MR than FLTP in most rounds, and has more stable round-wise performance than ALFLTP-NLL.

翻译：深度学习是自动驾驶车辆轨迹预测的首选方法。然而，其对数据的高需求本质上要求具备足够丰富且高质量的集中式数据集，这容易导致隐私泄露。此外，对于其预测模块严重依赖机器学习工具的安全关键型信息物理系统而言，不确定性感知变得日益重要。在本文中，我们通过使用具有不确定性感知全局目标的联邦学习（应用于连接自动驾驶车辆），放宽了数据收集要求并增强了不确定性感知能力。我们将此算法命名为FLTP。我们进一步引入ALFLTP，该算法通过使用主动学习技术自适应选择参与客户端来提升FLTP。我们考虑负对数似然（NLL）和偶然不确定性（AU）作为客户端选择指标。在Argoverse数据集上的实验表明，FLTP显著优于在本地数据上训练的模型。此外，ALFLTP-AU在训练回归损失方面收敛更快，在大多数轮次中，其NLL、minADE和MR指标均优于FLTP，并且相比ALFLTP-NLL具有更稳定的逐轮性能。