车辆轨迹预测的分布外泛化：一种社会交互因果模型框架

何鹏; 万志飞; 崔亚平; 吴大鹏; 王汝言; 潘孟铨

doi:10.12263/DZXB.20250827

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车辆轨迹预测的分布外泛化：一种社会交互因果模型框架

学术论文 | 更新时间：2026-07-30

- 车辆轨迹预测的分布外泛化：一种社会交互因果模型框架
- OOD Generalization of Vehicle Trajectory Prediction Based on Causal Model of Social Interaction
- 电子学报 2026年54卷第4期页码：1885-1899
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.先进网络与智能互联技术重庆市高校重点实验室，重庆 400065
  3.泛在感知与智能互联重庆市重点实验室，重庆 400065
- 作者简介：
  
  何鹏男，1990年5月出生于重庆市江津区。2018年获得电子科技大学博士学位。现为重庆邮电大学副教授。主要研究方向为新一代通信网络技术、跨学科通信交叉领域。在国内外发表学术论文40余篇。E-mail: hepeng@cqupt.edu.cn
  万志飞男，2000年11月出生于湖北省监利市。2024年获得重庆邮电大学学士学位。现为重庆邮电大学通信与信息工程学院硕士研究生。主要研究方向为车联网。E-mail: s240131143@stu.cqupt.edu.cn
  崔亚平男，1986年11月出生于河南省新乡市。2017年获得西南交通大学交通信息工程与控制博士学位。现为重庆邮电大学通信与信息工程学院副教授。主要研究方向为机器学习和车辆网络的智能通信。E-mail: cuiyp@cqupt.edu.cn
  吴大鹏男，1979年8月出生于黑龙江省大庆市。2009年获得北京邮电大学博士学位。现为重庆邮电大学通信与信息工程学院教授。主要研究方向为泛在无线网络、无线网络服务质量管理等。E-mail: wudp@cqupt.edu.cn
  王汝言男，1968年12月出生于湖北省浠水县。2007年获得中国电子科技大学博士学位。现为重庆邮电大学通信与信息工程学院教授。主要研究方向为网络性能分析和多媒体信息处理。中国电子学会会员编号：E190005780S。E-mail: wangry@cqupt.edu.cn
  潘孟铨男，2000年2月出生于江西省万安县。现为北京嘀嘀无线科技发展有限公司员工。2025年获得重庆邮电大学硕士学位。主要研究方向为车联网与机器学习。E-mail: s220101114@stu.cqupt.edu.cn
- 基金信息：
  
  国家自然科学基金(U24A20211;62271096);重庆市教委科学技术研究项目(KJQN202500603;KJQN202300621);重庆市自然科学基金(CSTB2025NSCQ-LZX0144;CSTB2024NSCQ-LZX0124;CSTB2023NSCQ-LZX0134);重庆市高校创新研究群体项目(CXQT20017);重邮信通青创团队支持计划(SCIE-QN-2022-04);四川省重点研发计划(2024YFHZ0093)
- DOI：10.12263/DZXB.20250827
  中图分类号： TP391;
- 收稿：2025-09-23，
  
  录用：2026-03-25，
  
  网络首发：2026-05-20，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
何鹏, 万志飞, 崔亚平, 等. 车辆轨迹预测的分布外泛化：一种社会交互因果模型框架[J]. 电子学报, 2026, 54(04): 1885-1899.

HE Peng, WAN Zhifei, CUI Yaping, et al. OOD Generalization of Vehicle Trajectory Prediction Based on Causal Model of Social Interaction[J]. Acta Electronica Sinica, 2026, 54(04): 1885-1899.
何鹏, 万志飞, 崔亚平, 等. 车辆轨迹预测的分布外泛化：一种社会交互因果模型框架[J]. 电子学报, 2026, 54(04): 1885-1899. DOI：10.12263/DZXB.20250827

HE Peng, WAN Zhifei, CUI Yaping, et al. OOD Generalization of Vehicle Trajectory Prediction Based on Causal Model of Social Interaction[J]. Acta Electronica Sinica, 2026, 54(04): 1885-1899. DOI：10.12263/DZXB.20250827

摘要

自动驾驶车辆在真实开放道路的可靠部署，高度依赖其在未知交通场景中的轨迹预测泛化能力，精准的车辆轨迹预测更是连接自动驾驶环境感知与决策规划模块的关键纽带，直接决定了自动驾驶系统的运行安全性与环境适应性。现有基于深度学习的预测方法主要依赖历史与未来轨迹间的统计相关性，其性能严重受限于训练与测试数据独立同分布（Independent and Identically Distributed，IID）的基本假设。然而，实际复杂交通环境中地图几何、道路拓扑结构等的显著差异导致数据分布偏移，IID假设难以成立，造成各类预测模型在分布外（Out-Of-Distribution，OOD）场景下性能显著下降，这成为自动驾驶系统开放道路部署的核心技术瓶颈。为应对上述挑战，本文提出一种即插即用的基于社会交互因果模型的车辆轨迹预测（Vehicle Trajectory Prediction based on Causal Model，CMVTP）框架。该框架可灵活嵌入现有主流轨迹预测模型，无需对原模型进行复杂修改或重训练，具备高度的通用性与适配性。CMVTP通过构建面向交通场景的结构因果模型（Structural Causal Model，SCM），显式刻画域不变因果特征与域可变因果特征间的内在关联，精准揭示轨迹数据中的潜在因果关系，有效提取驾驶风格、社会交互等跨环境稳定的域不变特征；同时设计因果特征学习模块实现域不变与域可变特征的显式分离，并提出双路径的不变特征转移模块，通过隐藏层特征对齐与输出分布对齐强化模型对域不变特征的提取能力，降低模型对地图特征等域可变特征的依赖。实验结果表明，CMVTP能有效提升多种主流预测模型的分布外泛化性能，在各类未见场景中，模型预测精度获得显著提升，最高可达12.75%。本文提出的CMVTP框架突破了传统基于统计相关性的轨迹预测方法在分布外场景的性能局限，为提升自动驾驶系统在开放动态环境中的适应性和安全性提供了重要技术支撑，也为车辆轨迹预测的泛化建模研究提供了新的思路与方法。

Abstract

The reliable deployment of autonomous vehicles on real-world open roads is highly dependent on their trajectory prediction generalization ability in unknown traffic scenarios. Accurate vehicle trajectory prediction serves as a key bridge between the autonomous driving environment perception and decision-making and planning modules

which directly determines the operational safety and environmental adaptability of autonomous driving system. The existing prediction methods based on deep learning mainly rely on the statistical correlation between historical and future trajectories

and their performance is severely limited by the basic assumption that training and test data are independent and identically distributed (IID). However

in complex real-world traffic environments

significant variations in map geometry and road topology lead to distribution shifts

making the IID assumption difficult to hold. Consequently

various prediction models suffer from substantial performance degradation in out-of-distribution (OOD) scenarios

which has become a critical technical bottleneck for the deployment of autonomous driving systems on open roads. To address these challenges

this paper proposes a plug-and-play vehicle trajectory prediction framework based on a causal model (CMVTP). The framework can be flexibly integrated into existing mainstream trajectory prediction models without requiring complex modifications or retraining

demonstrating strong generality and adaptability. By constructing a traffic scene-oriented structural causal model (SCM)

CMVTP explicitly characterizes the intrinsic relationships between domain-invariant and domain-variant causal features

accurately reveals the underlying causal relationships in trajectory data

and effectively extracts domain-invariant features such as driving style and social interactions. Meanwhile

a causal feature learning module is designed to explicitly disentangle domain-invariant and domain-variant features

and a dual-path invariant feature transfer module is proposed. The hidden layer feature alignment and output distribution alignment enhance the model’s ability to extract domain-invariant features and reduce its reliance on domain-variant features such as map characteristics. The experimental results show that CMVTP can effectively improve the out-of-distribution generalization performance of various mainstream prediction models. In various unseen scenarios

prediction accuracy is significantly improved

with gains of up to 12.75%. The CMVTP framework proposed in this paper breaks through the performance limitations of traditional trajectory prediction methods based on statistical correlations in out-of-distribution scenarios

provides important technical support for improving the adaptability and safety of autonomous driving systems in open dynamic environments

and offering new insights and methodologies for generalization modeling in vehicle trajectory prediction.

关键词

Keywords

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