Accurate Vehicle Trajectory Prediction is critical for automated vehicles and advanced driver assistance systems. Vehicle trajectory prediction consists of two essential tasks, i.e., longitudinal position prediction and lateral position prediction. There is a significant correlation between driving intentions and vehicle motion. In existing work, the three tasks are often conducted separately without considering the relationships between the longitudinal position, lateral position, and driving intention. In this paper, we propose a novel Temporal Multi-Gate Mixture-of-Experts (TMMOE) model for simultaneously predicting the vehicle trajectory and driving intention. The proposed model consists of three layers: a shared layer, an expert layer, and a fully connected layer. In the model, the shared layer utilizes Temporal Convolutional Networks (TCN) to extract temporal features. Then the expert layer is built to identify different information according to the three tasks. Moreover, the fully connected layer is used to integrate and export prediction results. To achieve better performance, uncertainty algorithm is used to construct the multi-task loss function. Finally, the publicly available CitySim dataset validates the TMMOE model, demonstrating superior performance compared to the LSTM model, achieving the highest classification and regression results. Keywords: Vehicle trajectory prediction, driving intentions Classification, Multi-task

翻译：精确的车辆轨迹预测对于自动驾驶车辆和高级驾驶辅助系统至关重要。车辆轨迹预测包含两个核心任务，即纵向位置预测和横向位置预测。驾驶意图与车辆运动之间存在显著相关性。在现有研究中，这三个任务通常被独立处理，未考虑纵向位置、横向位置和驾驶意图之间的关联。本文提出一种新颖的时序多门混合专家模型，用于同时预测车辆轨迹和驾驶意图。该模型由三个层级构成：共享层、专家层和全连接层。模型中，共享层利用时序卷积网络提取时序特征；专家层则根据三个任务分别识别不同信息；全连接层用于整合并输出预测结果。为实现更优性能，采用不确定性算法构建多任务损失函数。最后，通过公开的CitySim数据集验证了TMMOE模型，其性能优于LSTM模型，并取得了最高的分类和回归结果。关键词：车辆轨迹预测，驾驶意图分类，多任务