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Mamdani vs. Takagi-Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models.
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
Summary
This study compares Mamdani and Takagi-Sugeno fuzzy inference systems for calibrating car-following models in intelligent transportation systems. The Takagi-Sugeno system demonstrated superior accuracy in compensating for uncertainties, leading to more realistic traffic simulations.
Area of Science:
- Intelligent Transportation Systems (ITSs)
- Traffic Engineering
- Control Systems
Background:
- Urban traffic congestion necessitates intelligent transportation systems (ITSs) for improved traffic flow.
- Car-following models are crucial for traffic simulation, estimating behavior, and ensuring collision avoidance.
- Uncertainties in modeling and measurement introduce discrepancies between simulated and observed traffic data.
Purpose of the Study:
- To comparatively analyze Mamdani and Takagi-Sugeno fuzzy inference systems (FISs) for calibrating continuous-time car-following models.
- To propose a methodology for parallel data processing and evaluating FIS impact on vehicle dynamics.
- To assess the effectiveness of FIS calibration in enhancing autonomous driving solutions.
Main Methods:
- Developed a methodology for parallel data processing using Mamdani and Takagi-Sugeno FISs.
- Simulated a continuous-time car-following model in MATLAB Simulink, incorporating noise injection to mimic sensor errors.
- Evaluated FIS performance based on running distance and dynamic safety distance, comparing simulated to observed model behavior.
Main Results:
- Both Mamdani and Takagi-Sugeno FISs successfully calibrated the car-following model, reducing discrepancies.
- The Takagi-Sugeno FIS provided more accurate compensation values compared to the Mamdani FIS.
- The Takagi-Sugeno FIS resulted in simulated vehicle behavior that more closely matched the observed model.
Conclusions:
- Fuzzy inference systems are effective tools for calibrating car-following models and managing uncertainties in traffic simulations.
- The Takagi-Sugeno FIS offers superior performance for car-following model calibration due to its ability to yield more precise compensation values.
- Accurate calibration is vital for the development of reliable autonomous driving systems that depend on real-time data processing.
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