安庆师范大学学报（自然科学版）2026，Vol.32Issue(2)：63-71,9.DOI:10.13757/j.cnki.cn34-1328/n.2026.02.009
基于多尺度融合的车辆轨迹预测研究
Vehicle Trajectory Prediction Based on Multi-Scale Fusion
摘要
Abstract
In the current complex traffic environment,accurately predicting vehicle trajectories is crucial for advancing autonomous driving technology.To address the challenges of complex vehicle interactions and trajectory uncertainty in traffic scenarios,this study proposes a multi-scale fusion vehicle trajectory prediction model(MFVM).The model consists of a tem-poral encoder,a multi-scale fusion module(MFM),and an MFVM decoder.First,the temporal encoder captures long-term temporal dependencies.Next,multi-scale convolutional networks extract multi-granularity trajectory features,which are then fused using a spatiotemporal dual-channel attention mechanism.Finally,the fully connected layer and multi-layer perceptron(MLP)decoder generate predicted trajectories.Experimental results demonstrate that compared to existing models,the MFVM model reduces root mean square error by 0.09～0.19 meters in each prediction interval of 1～2 seconds and by 0.42～0.99 meters within 3～5 seconds,fully validating the trajectory prediction performance of this model,which possesses significant advantag-es and practical value especially in long-term prediction.关键词
运动预测/车辆轨迹预测/特征融合/自动驾驶/时间编码器Key words
motion prediction/vehicle trajectory prediction/feature fusion/autonomous driving/time encoder分类
交通工程引用本文复制引用
朱礼龙,谢进,孙西超..基于多尺度融合的车辆轨迹预测研究[J].安庆师范大学学报（自然科学版）,2026,32(2):63-71,9.基金项目
国家自然科学基金青年科学基金项目(62406095)和安徽省优秀科研创新团队项目(2024AH010017) （62406095）
