交通信息与安全2025，Vol.43Issue(2)：44-53,64,11.DOI:10.3963/j.jssn.1674-4861.2025.02.006
基于Transformer-TCN-GRU的场面滑行轨迹预测模型
A Model for Aircraft Surface Taxiing Trajectory Prediction Base on Transformer-TCN-GRU
摘要
Abstract
For aircraft taxiing trajectory prediction,existing methods exhibit low accuracy in real-time estimation of future positions over medium-term time horizons.To enhance prediction precision within this temporal scope while maintaining computational efficiency,this study proposes a taxiing trajectory prediction model integrating trans-former networks,cross-attention mechanisms,temporal convolutional networks(TCN),and gated recurrent units(GRU)to generate multiple candidate trajectories.The Transformer encoder captures temporal dependencies and motion patterns from historical trajectory data to derive global feature representations.Airport vector maps and taxi-ing route instructions from air traffic control systems are utilized to compute planned future taxiing path coordi-nates.A cross-attention mechanism then aligns the global trajectory features(as Query)with critical positions in the planned path sequence,mapping the fused path-enhanced features into multimodal representations corresponding to candidate trajectories.The TCN-GRU decoder processes each modality to capture long-term temporal dependencies and outputs multiple predicted trajectories with associated probabilities.Validation on real taxiing trajectories from a major Chinese airport demonstrates minimum average displacement error(minADE)of 1.932 m and minimum fi-nal displacement error(minFDE)of 1.811 m for 8-second predictions.Compared to individual GRU and TCN mod-els,the proposed approach reduces minADE/minFDE by 14.10%/30.88%and 16.62%/34.72%respectively,while maintain an average runtime of 17.70 milliseconds per sample.The proposed method achieves accurate and efficient trajectory prediction,supporting enhanced safety management in airport maneuvering areas.关键词
滑行轨迹/轨迹预测/Transformer模型/时间卷积网络/门控循环单元Key words
taxiing trajectory/trajectory prediction/transformer/temporal convolutional network/gated recurrent unit分类
航空航天引用本文复制引用
王兴隆,李国祥,张钊,叶可,苏婷,葛京..基于Transformer-TCN-GRU的场面滑行轨迹预测模型[J].交通信息与安全,2025,43(2):44-53,64,11.基金项目
天津市教育委员会自然科学重点项目(2020ZD01)资助 （2020ZD01）
