【摘要】 在大规模动态变化的交通场景下，快速准确地预测车辆驾驶行为是智能交通领域极具挑战的问题之一。车辆驾驶行为的预测不仅要考虑通信的有效性，而且要考虑车辆历史行驶轨迹以及车辆之间的相互影响。文中综合考虑了上述因素，提出了一种新的基于边-增强图卷神经网络的通信有效的分布式机器学习框架EGCN-CeDML(Edge-enhanced Graph Convolutional Neural Network-Communication-efficient Distributed Machine Learning)。相比面向单一设备的集中式预测框架，EGCN-CeDML是通信有效的分布式机器学习框架，该框架无需将所有原始数据发送到云服务器，而是直接将用户数据在本地边缘设备存储、处理和计算。这种在多个边缘设备训练神经网络的方式缓解了集中训练神经网络的压力，降低了传输数据量和通信延迟，提升了数据处理效率，在一定程度上也保护了用户隐私。各个边缘设备部署的复合图卷积网络(EGCN-LSTM)利用边-增强注意力机制和图卷积神经网络的特征传递机制，当周围车辆数量增长至十几辆时仍能快速提取和传递车辆间的交互信息，保证了较准确的预测性能和较低的时间复杂度。不限于车辆驾驶行为预测，各边缘设备可以根据自身的计算能能力和存储能力，在保证神经网络性能的前提下灵活控制神经网络的类型和规模以适用于不同的应用场景。EGCN-CeDML在公开数据集NGSIM上的实验结果表明：无论交通复杂程度如何，EGCN-CeDML的计算时间和预测性能都优于以往模型，精准率可达0.939 1,召回率可达0.955 7,F1分数可达0.947 3;预测时长为1 s时，预测准确率达到了91.21%;即使车辆数目增加，算法也能保持较低的时间复杂度，且稳定在0.1 s以内。更多还原

【Abstract】 In large-scale dynamic traffic scenarios, predicting vehicle driving behavior quickly and accurately is one of the most challenging issues in the field of intelligent traffic driving.The prediction of vehicle driving behavior should consider not only the efficiency of communication, but also the historical vehicle trajectory and the interaction between vehicles.Considering the above factors, this paper proposes a communication-efficient distributed machine learning framework based on edge-enhanced graph convolutional neural networks(EGCN-CeDML).Compared with the centralized prediction framework on a single device, EGCN-CeDML is a communication-efficient distributed machine learning framework, which does not need to transmit all the raw data to the cloud server, and directly stores, processes, and computes user data locally.This way of training neural networks on multiple edge devices relieves the pressure of centralized training neural networks, reduces the amount of transmitted data and communication latency, improves data processing efficiency, and preserves user privacy to a certain extent.EGCN-LSTM deployed on each edge device utilizes the edge-enhanced attention mechanism and the feature transfer mechanism of the graph convolutional neural network to promptly extract and transfer the interaction information between vehicles when the number of surrounding vehicles increases to more than a dozen, ensuring more accurate prediction performance and lower time complexity.In addition to vehicle driving behavior prediction, each edge device can flexibly control the type and scale of the neural network according to its own computing and storage capabilities, under the premise of ensuring the performance of the neural network, which is suitable for different application scenarios.The experimental results of EGCN-CeDML on public dataset NGSIMshow that the amount of data to be transmitted by only accounts for 21.56% of the centralized training.And the calculation time and prediction performance of EGCN-CeDML are better than those of previous models regardless of traffic complexity, with an accuracy rate of 0.939 1,a recall rate of 0.955 7,and an F1 score of 0.947 3.When the prediction time is one second, the prediction accuracy reaches 91.21%.Even if the number of vehicles increases, the algorithm maintains a low time complexity and is stable within 0.1 seconds.更多还原