A Fuzzy Monitoring and Diagnosis Process to Detect Evolutions of a Car Driver's Behavior

Abstract

This paper describes a monitoring and diagnosis process for the detection of evolution of a system whose characteristics vary with time. This is an adaptive fuzzy pattern recognition algorithm that achieves progressive and on-line learning of the system states. A neural net based architecture associated with a real-time algorithm for the detection of abrupt changes provides a diagnosis of evolution that may also be predictive. The algorithm was applied to the monitoring of a car driver’s behavior. Results on real data are reported.