Graph-Motif matrix enhanced conditional diffusion model for vehicle trajectory generation

Yuan Zhoulü; He Zhaocheng; Xie Chuanzhi

doi:10.11714/acta.snus.ZR20260066

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Graph-Motif matrix enhanced conditional diffusion model for vehicle trajectory generation

更新时间：2026-06-25

- Graph-Motif matrix enhanced conditional diffusion model for vehicle trajectory generation
- Acta Scientiarum Naturalium Universitatis Sunyatseni Pages: 1-10(2026)
- 作者机构：
  
  1.中山大学智能工程学院 / 广东省智能交通系统重点实验室，广东深圳 518107
  2.鹏城实验室，广东深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20260066
  CLC： U12
- Received：20 March 2026，
  
  Revised：2026-05-01，
  
  Accepted：04 May 2026，
  
  Online First：24 June 2026，
- 稿件说明：
移动端阅览
Yuan Zhoulü, He Zhaocheng, Xie Chuanzhi. Graph-Motif matrix enhanced conditional diffusion model for vehicle trajectory generation[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-10.
DOI：

Yuan Zhoulü, He Zhaocheng, Xie Chuanzhi. Graph-Motif matrix enhanced conditional diffusion model for vehicle trajectory generation[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-10. DOI： 10.11714/acta.snus.ZR20260066.

摘要

在实际智能交通系统中，一旦物联网出现故障就会引发数据中断，这凸显了轨迹数据生成算法的重要性。现有的两种轨迹生成方法均存在不足。基于物理模型的算法不能适应动态变化的交通场景，而数据驱动方法容易忽略车辆之间的交互影响。针对交叉口这类复杂场景，本文提出一种基于条件扩散模型的轨迹生成方法。该方法引入模体矩阵量化周围车辆对目标车辆的影响程度，使用图注意力机制准确识别关键邻居车辆信息，并将条件信息引入扩散模型的反向去噪过程中生成车辆轨迹。最后，在Nuscenes数据集和自建轨迹数据集上开展了验证实验。结果显示：本文方法在多个精度评价指标上均优于现有主流模型，且更加符合运动学规律。

Abstract

In practical intelligent transportation systems，failures of IoT may lead to data interruption， underscoring the importance of trajectory data generation algorithms. Existing trajectory generation methods can be categorized into two types， both of which exhibit limitations. Physics-based algorithms are unable to adapt to dynamically changing traffic scenarios， whereas data-driven methods tend to overlook interactions among vehicles. To address complex scenarios such as intersections， this paper proposes a trajectory generation method based on a conditional diffusion model. The proposed method introduces a motif matrix to quantify the influence of surrounding vehicles on the target vehicle，and employs a graph attention mechanism to accurately identify key neighboring vehicle information， and the conditional information is incorporated into the reverse denoising process of the diffusion model to generate vehicle trajectories. Experiments conducted on the Nuscenes dataset and a self-collected trajectory dataset demonstrate that the proposed method outperforms existing mainstream models across multiple accuracy evaluation metrics and is more consistent with kinematic principles.

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references

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