Aug 2026· 2026 3rd International Conference on Image Processing, Multimedia Technology and Machine Learning (IPMML)· pp. 158-165· 0 citations· 24 references
Abstract
Highway ramp merging zones represent a typical dense traffic scenario where complex vehicle interactions make accurate trajectory prediction particularly challenging. This paper proposes a novel dual-encoder model with an interactive time horizon mechanism to address these challenges. The model employs independent encoding channels to separately capture individual motion features and multi-vehicle interaction features, while the introduced selector enables dynamic selection of potentially interacting vehicles. Evaluated on the Chinese highway dataset Expressway-SQM2, our approach demonstrates significant improvements: the dual-encoder architecture alone reduces 5-second prediction RMSE by 29.4% compared with the baseline model, while the complete model with this mechanism achieves a 34.5% reduction. Experimental results confirm the model's effectiveness in handling complex merging scenarios and its strong generalization capability across varying traffic conditions.
G-VTM, a generalized vision-trajectory model, is proposed, which captures global map semantics while modeling scenario-and direction-aware interaction based on intuitive visual perception and achieves strong generalized performance under heterogeneous traffic conditions.
Xinyue Zhang, Letian Gong, Yan Lin et al.· Proceedings of the Thirty-Fi...· 0 citations
A non-local multi-head spatiotemporal attention based long short-term memory model (NL-MHA-LSTM) is introduced which employs an attention mechanism to assign context weights to relevant neighbor vehicles and extends beyond pairwise effects to model long-range dependencies.
S. Rashid, M. A. Khan, Usman Akram et al.· PLoS ONE· 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 kine...
Da Zheng, Bing-Li Zhang, Xinyu Wang et al.· Engineering Research Express· 0 citations
A framework that fuses the output of Trajectron++, a neural network-based trajectory predictor, with extended Kalman filter (EKF)-based multiple trajectory candidates at a late stage indicates that EKF-based trajectory candidates can effectively complement neural trajectory prediction through learned fusion.
Seong-Jun Kim, Seung-Hyun Kong· Journal of Institute of Cont...· 0 citations
A novel hybrid deep learning framework suitable for cloud-based control platforms, providing a foundational algorithmic solution for vehicle-infrastructure cooperative perception and decision-making and suggests potential for integration into intelligent transportation cloud control platforms.
Zi-Yan Liang, Rui Yuan, Peng-Ying Zhou et al.· SAE technical paper series· 0 citations
A long-term Trajectory prediction multi-scale AutoRegressive framework (TrajAR), which follows an encoder-decoder structure that progressively refines the predicted trajectory from coarse to fine scales, where coarse-scale predictions establish long-term path backbones and finer scales enhance precision.
Letian Gong, Yan Lin, Xinyue Zhang et al.· Proceedings of the Thirty-Fi...· 0 citations
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