基于多特征融合的车辆轨迹预测研究

doi:10.19678/j.issn.1000-3428.0070065

摘要/Abstract

摘要： 针对现有模型对车辆特征提取不足和预测场景单一的问题,提出了一种在多场景下融合多特征的车辆轨迹预测模型MTF-GRU-MTSHMA。该模型由编码器模块、多特征提取模块、多特征融合模块和轨迹预测模块组成。在编码器模块,利用门控循环单元(GRU)对车辆历史信息进行编码得到车辆的历史状态;在多特征提取模块,考虑目标车辆区域内周围车辆之间的空间关联性,通过多维度空间注意力机制挖掘周围车辆的深层特征,并引入三重注意力机制对编码后的状态向量进行特征提取;在多特征融合模块,将提取到的多种特征进行线性拼接,并输入到多特征融合网络中进行融合;在轨迹预测模块,对GRU进行改进,提出混合示教门控循环单元(MTF-GRU)并作为解码器,通过引入示教率来控制解码模式以提高解码性能,将融合后的特征输入到解码器中生成未来轨迹。在NGSIM数据集上进行的仿真实验结果表明,与最优基准模型相比,所提模型在直线道路、十字路口以及环岛道路场景下的均方根误差(RMSE)分别提高了8.16%、10.31%和8.37%,证明了所提模型的有效性。

关键词: 轨迹预测, 注意力机制, 多特征融合, 混合示教门控循环单元, 解码模式

Abstract: In response to the problems of insufficient vehicle feature extraction and single prediction scenarios in existing models, this paper proposes a vehicle trajectory prediction model, called MTF-GRU-MTSHMA, that integrates multiple features in multiple scenarios. The proposed model consists of an encoder module, multifeature extraction module, multifeature fusion module, and trajectory prediction module. In the encoder module, the Gated Recurrent Unit (GRU) is used to encode the historical information of the vehicle to obtain its historical status. In the multifeature extraction module, considering the spatial correlation between surrounding vehicles in the target vehicle area, a multidimensional spatial attention mechanism is proposed to mine the deep features of surrounding vehicles. Additionally, a triple attention mechanism is introduced to extract features from the encoded state vector. In the multifeature fusion module, the extracted multiple features are linearly concatenated and input into the multifeature fusion network for fusion. In the trajectory prediction module, improvements are made to the GRU by proposing a Mixed Teaching Force Gated Recurrent Unit (MTF-GRU) as the decoder, which controls the decoding mode by introducing a teaching rate to improve decoding performance. The fused features are input into the decoder to generate future trajectories. The proposed model is experimentally simulated using the NGSIM dataset. The results show that the average Root Mean Square Error (RMSE) of the proposed model for straight road, intersection, and roundabout road scenarios increases by 8.16%, 10.31%, and 8.37%, respectively, compared with the optimal benchmark model, proving the effectiveness of the proposed model.

Key words: trajectory prediction, attention mechanism, multifeature fusion, Mixed Teaching Force Gated Recurrent Unit (MTF-GRU), decoding mode

中图分类号:

TP18

王庆荣, 郝福乐, 朱昌锋, 王俊杰. 基于多特征融合的车辆轨迹预测研究[J]. 计算机工程, 2026, 52(2): 331-341.

WANG Qingrong, HAO Fule, ZHU Changfeng, WANG Junjie. Research on Vehicle Trajectory Prediction Based on Multifeature Fusion[J]. Computer Engineering, 2026, 52(2): 331-341.

https://www.ecice06.com/CN/Y2026/V52/I2/331

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