Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers' driving styles and vehicle types, speed predictions for different target vehicles may significantly differ. Existing methods may not realize personalized vehicle speed prediction while protecting drivers' data privacy. We propose a Federated learning framework with Personalized Aggregation Weights (FedPAW) to overcome these challenges. This method captures client-specific information by measuring the weighted mean squared error between the parameters of local models and global models. The server sends tailored aggregated models to clients instead of a single global model, without incurring additional computational and communication overhead for clients. To evaluate the effectiveness of FedPAW, we collected driving data in urban scenarios using the autonomous driving simulator CARLA, employing an LSTM-based Seq2Seq model with a multi-head attention mechanism to predict the future speed of target vehicles. The results demonstrate that our proposed FedPAW ranks lowest in prediction error within the time horizon of 10 seconds, with a 0.8% reduction in test MAE, compared to eleven representative benchmark baselines. The source code of FedPAW and dataset CarlaVSP are open-accessed at: https://github.com/heyuepeng/PFLlibVSP and https://pan.baidu.com/s/1qs8fxUvSPERV3C9i6pfUIw?pwd=tl3e.

翻译：车辆速度预测对于智能交通系统至关重要，通过准确预测未来车辆状态，能够提升自动驾驶的可靠性。由于驾驶员驾驶风格与车辆类型的差异，不同目标车辆的速度预测结果可能存在显著区别。现有方法在保护驾驶员数据隐私的同时，往往难以实现个性化的车辆速度预测。为应对这些挑战，本文提出一种具有个性化聚合权重的联邦学习框架（FedPAW）。该方法通过度量本地模型与全局模型参数之间的加权均方误差，捕获客户端特定信息。服务器向客户端发送定制化的聚合模型而非单一全局模型，且不会为客户端带来额外的计算与通信开销。为评估FedPAW的有效性，我们利用自动驾驶仿真器CARLA采集城市场景驾驶数据，采用基于LSTM的Seq2Seq模型结合多头注意力机制预测目标车辆的未来速度。实验结果表明，在10秒时间范围内，与十一种代表性基准方法相比，我们提出的FedPAW在预测误差上表现最优，测试平均绝对误差降低了0.8%。FedPAW源代码及数据集CarlaVSP已公开于：https://github.com/heyuepeng/PFLlibVSP 与 https://pan.baidu.com/s/1qs8fxUvSPERV3C9i6pfUIw?pwd=tl3e。