App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Multimodal trajectory prediction based on lane feature calibration and adaptive Fusion

Yang Xiaosong
Ding Derui
Zhou Yang
Liu Xue
School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2026.04.0081

Abstract

To address the difficulty of modeling complex interactions in multi-agent traffic scenarios and the prediction instability caused by noise, this study developed a multimodal trajectory prediction model based on lane feature calibration and adaptive gated fusion, named Lane Calibration and Adaptive Fusion Network (LCAF-Net) . First, LCAF-Net introduced a lane feature calibration (LFC) module during encoding and employed residual calibration to improve the discriminability of lane features under complex road structures. Second, LCAF-Net employed an adaptive multi-context fusion (AMCF) strategy to dynamically adjust the weights of historical motion information and map structural information according to scenario variations, thereby generating more reliable multimodal features. Experiments on the INTERACTION dataset show that LCAF-Net reduced minJointFDE and minJointADE by 5.3% and 6.1%, respectively, compared with HPNet. On the Argoverse dataset, LCAF-Net outperformed other models on three commonly used metrics. These results demonstrate its favorable prediction performance in complex interactive traffic scenarios. The code is available at: https: //github. com/yyxsxxx/LCAFNet.

Foundation Support

国家自然科学基金资助项目(62373251,62203306)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0081
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-17] Accepted Paper

Cite This Article

仰晓松, 丁德锐, 周洋, 等. 基于车道特征校准与自适应融合的多模态轨迹预测 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0081. (Yang Xiaosong, Ding Derui, Zhou Yang, et al. Multimodal trajectory prediction based on lane feature calibration and adaptive Fusion [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0081. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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