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| Vehicle trajectory prediction model integrating dynamic risk map and multivariate attention mechanism |
Wenqiang CHEN1( ),Linyue FENG2,Dongdan WANG2,Yulei GU3,*(),Xuan ZHAO3 |
1. School of Future Transportation, Chang’an University, Xi’an 710064, China
2. School of Transportation Engineering, Chang’an University, Xi’an 710064, China
3. School of AutoMobile, Chang’an University, Xi’an 710064, China |
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Abstract A multi-target trajectory cooperative prediction model (RGMA) based on a dynamic risk map and multivariate attention mechanism was proposed aiming at the problems of insufficient accuracy and generalization ability in vehicle trajectory prediction in complex traffic scenarios. A dynamic risk graph that integrated multi-factor interaction features such as vehicle size, speed, acceleration, and heading angle was constructed, and the conflict risk between vehicles was quantified as the adjacency weight of the graph convolutional network, enhancing the physical interpretability of spatial interaction modeling. A multivariate attention Transformer module was designed to treat the time series of each variable as an independent token, capturing cross-variable dependency and long-term temporal feature in order to improve temporal modeling capability. The future trajectories of multiple vehicles were output through concatenating the spatiotemporal feature and a multilayer perceptron. Experiments on real-world dataset NGSIM and HighD show that RGMA outperforms existing mainstream methods in both short-term and long-term prediction. Ablation study verifies the effectiveness of each module and the robustness of the model.
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Received: 23 September 2025
Published: 04 February 2026
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| Fund: 国家重点研发计划资助项目(2024YFB2505703)；国家自然科学基金资助项目（52172362)；陕西省自然科学基金资助项目(2025JC-YBMS-374)；中央高校基本科研业务费专项资金资助项目（300102344203)；陕西省交通运输厅科技资助项目（ZYXZB-20230223）. |
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Corresponding Authors:
Yulei GU
E-mail: cwq@chd.edu.cn;guylei001@chd.edu.cn
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融合动态风险图与多变量注意力机制的车辆轨迹预测模型
针对复杂交通场景中车辆轨迹预测精度与泛化能力不足的问题，提出基于动态风险图和多变量注意力机制融合的车辆多目标轨迹协同预测模型（RGMA）. 该模型通过构建动态风险图，融合车辆尺寸、速度、加速度和角度等多因素交互特征，量化车辆间的冲突风险作为图卷积网络的邻接权重，增强空间交互建模的物理可解释性. 设计多变量注意力Transformer模块，将各变量时间序列作为独立token，捕捉跨变量依赖与长时序特征，提升时间维度建模的能力. 通过拼接时空特征并经由多层感知机输出多车辆未来轨迹. 在NGSIM和HighD真实数据集上的实验表明，RGMA在短期与长期预测中均优于现有的主流方法，通过消融实验验证了各模块的有效性与模型鲁棒性.
关键词：
车辆轨迹预测,
动态风险图,
多变量注意力机制,
自动驾驶系统,
图神经网络
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