面向高移动性车联边缘系统轨迹感知的动态卸载

doi:10.16182/j.issn1004731x.joss.25-0844

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (7): 1935-1949.doi: 10.16182/j.issn1004731x.joss.25-0844

面向高移动性车联边缘系统轨迹感知的动态卸载

许浩然¹^,²^,³, 黄智钦¹^,²^,³, 胡友武¹^,²^,³, 陈哲毅¹^,²^,³

^1.福州大学计算机与大数据学院，福建福州 350116
^2.大数据智能教育部工程研究中心，福建福州 350002
^3.福建省网络计算与智能信息处理重点实验室(福州大学)，福建福州 350116

收稿日期:2025-09-03 修回日期:2026-01-10 出版日期:2026-07-28 发布日期:2026-07-31
通讯作者: 陈哲毅
第一作者简介:许浩然(2002-)，男，硕士生，研究方向为边缘计算、车联网、计算卸载。
基金资助:
国家自然科学基金(62202103);福建省杰出青年科学基金(2025J010020);中央引导地方科技发展资金(2022L3004)

Trajectory-aware Dynamic Offloading for High-mobility Vehicular Edge Systems

Xu Haoran¹^,²^,³, Huang Zhiqin¹^,²^,³, Hu Youwu¹^,²^,³, Chen Zheyi¹^,²^,³

^1.College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China
^2.Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China
^3.Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China

Received:2025-09-03 Revised:2026-01-10 Online:2026-07-28 Published:2026-07-31
Contact: Chen Zheyi

摘要/Abstract

摘要：

针对车联边缘计算(vehicular edge computing, VEC)中智能车辆(intelligent vehicles, IVs)高移动性导致的服务中断、任务失败及资源浪费问题，提出一种轨迹感知的动态卸载框架(trajectory-aware dynamic offloading framework for high-mobility vehicular edge systems, TADO)。设计一种轻量级T-pattern轨迹感知算法，通过挖掘车辆历史轨迹中的时空模式以高效预测下一跳路侧单元(road side unit, RSU)，为卸载决策提供前瞻性参考；构建联合优化模型，并引入轨迹预测驱动的服务中断风险因子；设计一种改进的DRL方法，将预测轨迹作为关键状态输入，结合参数空间噪声探索与优先经验回放机制，以学习高动态环境下的前瞻性与鲁棒性卸载策略。基于真实城市轨迹数据集的仿真实验表明：与本地计算、边缘全卸载等基准方法相比，所提TADO框架在不同场景下均能明显降低任务处理延迟与系统能耗，并维持更高的任务完成率。

关键词: 车联边缘计算, 轨迹预测, 计算卸载, DRL

Abstract:

To address the problems of service interruptions, task failures, and resource waste caused by high mobility of intelligent vehicles (IVs) in vehicular edge computing (VEC), this paper proposes a trajectory-aware dynamic offloading (TADO) framework for high-mobility vehicular edge systems. A lightweight T-pattern trajectory-aware algorithm is designed to efficiently predict the next-hop road side unit (RSU) by mining spatio-temporal patterns from historical trajectories of vehicles, offering a forward-looking reference for offloading decisions. A joint optimization model is constructed, and a service interruption risk factor driven by trajectory prediction is introduced. An improved DRL method is developed. It takes the predicted trajectories as key state inputs and integrates parameter space noise exploration with a prioritized experience replay mechanism to learn forward-looking and robust offloading strategies in highly dynamic environments. Simulation experiments based on real-world datasets of urban vehicle trajectories demonstrate that, compared to benchmark methods, such as local computing and full edge offloading, the TADO framework significantly reduces task processing latency and system energy consumption and maintains a high task completion rate across various scenarios.

Key words: vehicular edge computing, trajectory prediction, computation offloading, DRL

中图分类号:

TP393

许浩然,黄智钦,胡友武等 . 面向高移动性车联边缘系统轨迹感知的动态卸载[J]. 系统仿真学报, 2026, 38(7): 1935-1949.

Xu Haoran,Huang Zhiqin,Hu Youwu,et al . Trajectory-aware Dynamic Offloading for High-mobility Vehicular Edge Systems[J]. Journal of System Simulation, 2026, 38(7): 1935-1949.

图/表 12

图1

图2

表1

仿真参数设置

仿真参数	设置
车辆 $v i$ 的传输功率 $P i$ /W	[0.4,0.6]^[18]
车辆 $v i$ 的本地计算能力 $f i l o c$ / $G H z$	[1.0,2.0] ^[19]
任务输入数据 $d i$ /MB	[0.4,1.2] ^[5]
任务计算复杂度 $c i$ /( $c y c l e s / b i t$ )	[200,1 000]
最大容忍延迟 $t i m a x$ / $s$	[1.5,3.0] ^[5,23]
高斯噪声功率 $σ 2$ /W	10^‒9[21]
单位距离信道增益 $g 0$ /dB	-30^{[18, 21]}
$λ 1$	0.7^[19]
$λ 2$	0.2^[19]
$λ 3$	0.1
经验回放池R	256
优先级权重 $ε$	0.6
重要性采样权重指数 $β$	0.4

表1

图3

表2

图4

图5

图6

图7

表3

图8

图9

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方法	精度/%	时间开销/ms
T-pattern	78.42	0.12
LSTM	84.60	12.45
Transformer	86.50	16.78

方法	任务最大容忍延迟/s
方法	1.5	2.0	3.0
TADO	62.31±1.12	78.80±0.95	92.68±0.82
DDPG	59.26±1.45	71.10±1.33	87.32±1.21
GA	55.96±1.87	69.76±1.56	85.71±1.54
EO	47.54±0.55	58.99±0.48	82.45±0.42
RO	49.12±0.63	60.34±0.59	79.20±0.51
LO	45.92±0	55.13±0	78.82±0

面向高移动性车联边缘系统轨迹感知的动态卸载

Trajectory-aware Dynamic Offloading for High-mobility Vehicular Edge Systems

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