This brief introduces a fuzzy sensor validation and fusion methodology and applies it to automated vehicle control in Intelligent Vehicle Highway Systems (IVHS). Sensor measurements are assigned confidence values through sensor-specific dynamic validation curves. The validation curves attain minima of zero at the boundaries of the validation gate. These in turn are determined by the largest physically possible change a system—in our example vehicles of the IVHS—can undergo in one time step. A fuzzy exponential weighted moving average time series predictor determines the location of the maximum value of the validation curves. Sensor fusion is then performed using a weighted average of sensor readings and confidence values, and—if available—the functionally redundant values calculated from other sensors.
Sensor Validation and Fusion for Automated Vehicle Control Using Fuzzy Techniques
Contributed by the Dynamic Systems and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. Manuscript received by the Dynamics Systems and Control Division February 10, 1998. Associate Editor: S. Fassois.
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Goebel , K. F., and Agogino , A. M. (February 10, 1998). "Sensor Validation and Fusion for Automated Vehicle Control Using Fuzzy Techniques ." ASME. J. Dyn. Sys., Meas., Control. March 2001; 123(1): 145–146. https://doi.org/10.1115/1.1343909
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