汽车工程

汽车工程 ›› 2024, Vol. 46 ›› Issue (1): 75-83.doi: 10.19562/j.chinasae.qcgc.2024.01.008

• 精选论文 • 上一篇    下一篇

面向车载时间敏感网络的流量调度策略及改进算法研究

张旭东1,2(),温雅1,2,邹渊1,2,孙文景1,2,张兆龙3,唐风敏4,刘卫国4,5   

  1. 1.北京理工大学机械与车辆学院，北京 100081
    2.北京理工大学，电动车辆国家工程研究中心，北京 100081
    3.北京新能源汽车股份有限公司，北京 100176
    4.国汽（北京）智能网联汽车研究院有限公司，北京 100176
    5.浙江大学，杭州 310058
  • 收稿日期:2023-03-26 修回日期:2023-07-19 出版日期:2024-01-25 发布日期:2024-01-23
  • 通讯作者: 张旭东 E-mail:xudong.zhang@bit.edu.cn
  • 基金资助:
    国家重点研发计划(2021YFB2500900)

Research on Traffic Scheduling Strategies and Improved Algorithms for In-Vehicle Time-Sensitive Networks

Xudong Zhang1,2(),Ya Wen1,2,Yuan Zou1,2,Wenjing Sun1,2,Zhaolong Zhang3,Fengmin Tang4,Weiguo Liu4,5   

  1. 1.School of Mechanical Engineering，Beijing Institute of Technology，Beijing 　100081
    2.Beijing Institute of Technology，National Engineering Research Center for Electric Vehicles，Beijing 　100081
    3.Beijing Electric Vehicle Co. ，Ltd. ，Beijing 　100176
    4.National Innovation Center of Intelligent and Connected Vehicles，Beijing 　100176
    5.Zhejiang University，Hangzhou 　310058
  • Received:2023-03-26 Revised:2023-07-19 Online:2024-01-25 Published:2024-01-23
  • Contact: Xudong Zhang E-mail:xudong.zhang@bit.edu.cn

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摘要：

本文面向汽车电子电气架构中的时间敏感网络（TSN）流量调度问题开展研究。针对实际应用需求，提出一种车载TSN网络拓扑建立方法。针对网络中多类型信息流调度问题，提出一种基于时间感知整形器（TAS）机制的流量调度策略并建立相应的数学模型，在降低网络总延时的同时，兼顾高优先级信息流的时间敏感性和低优先级信息流的数据完整性。为解决模型中信息流转发过程复杂导致求解效率不稳定和流量调度方案众多导致寻优困难的问题，提出一种改进的遗传算法（IGA），从设置自适应交叉概率公式、引入禁忌搜索变异、多种群联合3个方面进行了优化。实验结果表明，本文所提出的算法在端到端延时优化方面提升了43.47%，在生成方案稳定性方面提升了76.96%，该算法可得到低延时、高可靠的车载TSN流量调度方案。本文的研究成果为智能网联汽车领域的研究和车载网络通信算法的优化提供了思路。

关键词: 时间敏感网络, 流量调度, 遗传算法, 禁忌搜索

Abstract:

The traffic scheduling problem in time-sensitive networking （TSN） of automotive electrical and electronic architecture is investigated in this paper. To meet practical application requirements， a method for establishing the topology of in-vehicle TSN network is proposed. To address the multi-type traffic scheduling problem in the network， a traffic scheduling strategy based on the Time-Aware Shaper （TAS） mechanism is proposed， and the corresponding mathematical model is established， to reduce the total network delay while considering both the time sensitivity of high-priority traffic and the data integrity of low-priority traffic. To solve the problems of unstable solution efficiency caused by the complex information flow forwarding process in the model and the difficulty of optimization caused by numerous traffic scheduling solutions， an improved genetic algorithm （IGA） is proposed which is optimized from the aspects of setting adaptive crossover probability formula， introducing in taboo search mutation， and combining multiple populations. The experimental results show that the proposed algorithm improves the optimality by 43.47% in end-to-end latency optimization and the solution generation stability by 76.96%. The algorithm can obtain low-latency and high-reliability traffic scheduling solutions for in-vehicle TSN. The research findings of this paper provide insights for the study of intelligent connected vehicles and the optimization of in-vehicle network communication algorithms.

Key words: time-sensitive network（TSN）, traffic scheduling, genetic algorithm, taboo search