马姆达尼与塔卡吉-苏杰诺模糊推理系统在连续时间汽车跟踪模型的校准中
Mădălin-Dorin Pop1, Dan Pescaru1, Mihai V Micea1
1Computer and Information Technology Department, Politehnica University of Timisoara, 300223 Timisoara, Romania.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究比较了Mamdani和Takagi-Sugeno模糊推理系统,用于对智能交通系统中的汽车追踪模型进行校准. 塔卡吉-苏格诺系统在弥补不确定性方面表现出卓越的准确性,从而实现了更现实的交通模拟.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 交通工程是交通工程.
- 控制系统 控制系统
背景情况:
- 城市交通拥堵需要智能交通系统 (ITS) 来改善交通流动.
- 汽车追踪模型对于交通模拟,估计行为和确保避免碰撞至关重要.
- 建模和测量中的不确定性导致模拟和观察到的交通数据之间的差异.
研究的目的:
- 为了比较分析Mamdani和Takagi-Sugeno模糊推理系统 (FISs) 校准连续时间的汽车追踪模型.
- 提出一种用于并行数据处理和评估FIS对车辆动态影响的方法.
- 评估FIS校准对提高自动驾驶解决方案的有效性.
主要方法:
- 开发了使用Mamdani和Takagi-Sugeno FIS并行数据处理的方法.
- 在MATLAB Simulink中模拟了一个连续时间的汽车追踪模型,结合噪声注入来模拟传感器错误.
- 根据跑步距离和动态安全距离评估FIS性能,将模拟与观察模型行为进行比较.
主要成果:
- 马姆达尼和塔卡吉-苏格诺FIS都成功校准了汽车跟踪模型,减少了差异.
- 与Mamdani FIS相比,Takagi-Sugeno FIS提供了更准确的补偿值.
- 塔卡吉-苏格诺FIS导致模拟的车辆行为更接近观察到的模型.
结论:
- 模糊推理系统是校准汽车跟踪模型和管理交通模拟中的不确定性有效的工具.
- 塔卡吉-苏格诺FIS为汽车后续模型校准提供了卓越的性能,因为它能够产生更精确的补偿值.
- 精确的校准对于开发可靠的自动驾驶系统至关重要,这些系统依赖于实时数据处理.
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