Comprehensive experiments on the NGSIM dataset validate the proposed model, demonstrating robust performance across structured highway driving scenarios and both the accuracy and computational efficiency of the proposed architecture.
Abstract
Accurate vehicle trajectory prediction is essential for autonomous driving systems. However, in dynamic traffic environments, existing methods often fail to fully capture complex spatiotemporal interactions, resulting in error accumulation and degraded stability over extended prediction horizons. To address these limitations, we propose a hybrid CNN-MTF-LSTM architecture with an integrated attention mechanism. The model comprises three core components: 1) a dual-branch encoder that separately processes temporal dependencies through LSTM and spatial interactions through CNN; 2) a Bahdanau attention module that dynamically selects the most relevant historical context at each decoding step; and 3) a multi-task fusion decoder, which is trained using a hybrid teacher-forcing strategy, to output kinematically consistent predictions of future positions, velocities, and accelerations. Comprehensive experiments on the NGSIM dataset validate the proposed model, demonstrating robust performance across structured highway driving scenarios. Comparative results against several baseline methods confirm both the accuracy and computational efficiency of the proposed architecture.
An efficient Mamba-based feature extraction framework for jointly encoding vehicle trajectories and map information is proposed and achieves superior performance in terms of minADE, minFDE, and minMR, while maintaining high computational efficiency.
J. Li, L. Wang, J. Pei· Revista Internacional de Mét...· 0 citations
A Stochastic Gating Decoder for multimodal latent variable sampling, adaptively fusing kinematics and data-driven paths to capture driver intention uncertainty while maintaining kinematic consistency is introduced.
To address the strong dependence of space object orbit prediction on physical models and initial conditions, as well as the difficulty of completely eliminating prediction errors, this study proposes a satellite orbit prediction correction method that integrates an attention mechanism with a long short-term memory (LSTM) network. Taking the LAGEOS satellite as the research object, the proposed method uses position error, velocity, and acceleration features extracted from historical orbital data to train a deep learning model for predicting one-day-ahead orbital errors and correcting the SGP4 orbit prediction results. The experimental results show that the ATLSTM model outperforms the LSTM, support vector machine (SVM), back propagation neural network (BP), and bidirectional long short-term memory (BiLSTM) models in both orbital error prediction and correction. The residual ratios of ATLSTM in the X, Y, and Z axes are reduced to 3.68%, 4.77%, and 2.37%, respectively, effectively improving the accuracy of satellite orbital error prediction. Further analysis indicates that a reasonable setting of the number of neurons helps improve model performance, while the prediction difficulty increases with the extension of the prediction duration, suggesting that the ATLSTM model is more suitable for short-term orbital error prediction and correction. In addition, validation results for satellites at different orbital altitudes demonstrate that the proposed model has certain generalization capability. In summary, combining deep learning methods with physical orbital models can effectively improve the accuracy of space object orbit prediction and provides an effective approach for orbital error prediction, space situational awareness, and collision warning.
Qingshan Luo, Jiahao Ji, Tao Yang et al.· PLoS ONE· 0 citations
The results indicate that the multi-task CNN-LSTM can balance macroscopic behavior prediction and microscopic risk recognition, thereby improving the active warning capability of autonomous-driving systems in complex traffic scenarios.
Tianqing Liu, Xin-Yan Huang, Li-Fang He et al.· International Conference on...· 0 citations
A vision-based end-to-end autonomous parking framework trained through imitation learning that introduces a historical context fusion encoder to capture temporal dependencies from past vehicle motions, a dual-stream attention decoder to enhance interaction between scene features and trajectory representations, and kinematic-aware auxiliary losses to enforce smooth and feasible trajectory generation.
Daisy X. M. Zheng, Bingli Zhang, Xinyu Wang et al.· Engineering Research Express· 0 citations