基于自适应框架细分算法的车载以太网消息调度的研究
Jiaoyue Chen1, Yujing Wu1, Yihu Xu1
1College of Engineering, Yanbian University, Yanji 133002, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
一个新的自适应框架分割 (AFS) 算法通过优化时间敏感网络 (TSN) 调度,将车载以太网带宽利用率提高到94.16%. 这提高了智能驾驶系统的实时性能.
科学领域:
- 汽车工程 汽车工程
- 计算机网络 计算机网络
- 实时系统 实时系统
背景情况:
- 传统的车载网络 (LIN,CAN,FlexRay) 缺乏用于智能驾驶的带宽和速度.
- 车载以太网对于下一代汽车通信至关重要,因为它具有高带宽和低延迟.
- 现有的车载以太网调度方法在带宽利用方面存在局限性.
研究的目的:
- 为车载以太网优化时间敏感网络 (TSN) 调度.
- 为了提高带宽的利用率,并减少由防护带和先发制造造成的浪费.
- 提高车载通信网络的实时性能和响应能力.
主要方法:
- 开发基于TSN协议的创新自适应框架分割 (AFS) 算法.
- 灵活的分割和高效的消息调度,以优化车载以太网性能.
- 对AFS算法与现有调度方法进行实验性评估.
主要成果:
- AFS算法实现了94.16%的平均本地带宽利用率.
- 在AFS中,相对于Frame Preemption (4.35%),PAS (5.65%) 和改善的Qbv (30.48%) 进行了显著的改进.
- 该算法在复杂的网络流量条件下被证明是稳定的和高效的.
结论:
- AFS算法有效地减少了带宽浪费,并提高了车载以太网的实时功能.
- 这项研究为智能互联汽车的高效通信提供了至关重要的技术支持.
- 该研究促进了智能驾驶的车载以太网技术的开发和应用.
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