Session name: "Automated Highway Systems and Associated Advanced Vehicle Control Systems", part of the Dedicated Conference on Fuzzy Systems/Soft Computing in the Automotive and Transportation Industries.
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Alice Agogino
University of California at Berkeley
Department of Mechanical Engineering
USA
This paper describes a fuzzy logic based architecture for real time sensor validation and fusion for vehicle following tasks for automated highways. Automated highways and more generally "smart vehicles" rely heavily on sensor data for a variety of control purposes. High sensor data fidelity is of prime concern because human life is at stake. However, sensor data are always uncertain to some extent due to noise and possible sensor failures. We address these issues by proposing to validate and fuse multiple sensor readings using a fuzzy time series prediction model, fuzzy validation gates, and a weighted average fusion scheme. The integration of these methods allows the assignment of degrees of confidence to each sensor reading. This is achieved through the use of validation gates which are areas in which the reading is expected to lie. They take into account the specific properties of each sensor as well as the physical limitations of the system considered. The placement of the validation gates in turn is dependent of the fuzzy time series prediction which uses fused past values and sensor readings as input. Examples from experiments performed for PATH (Partners for Automated Transportation Highways) California show that this method works successfully under a variety of operating conditions.
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