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Driving style recognition using fuzzy logic

Detection and classification of aggressive driving can be based on the use of physiological signals or biometric information like electrocardiogram, electro dermal activity, and respiration. This research proposes a driving performance inference system based on the signature of acceleration in the two dimensions and speed. Driving style can be categorized to: below normal, normal, aggressive, and very aggressive. One of the targets of this paper is to recognize the driving events that fall into each of these categories. Many of driving styles are a major cause of traffic crashes. Many of these styles can be detected with reasonable accuracy using driving performance. The main idea is to utilize the 2-axis accelerometer that is embedded in most of the GPS tracker devices that are used in vehicle tracking and fleet management to recognize the driving styles. This method can be utilized for vehicle active safety purpose. Fuzzy logic inference system is used in classification of the extracted feature to the predefined driving styles. the power of the Euclidean norm of longitudinal and lateral is used as input to the fuzzy inference system in addition to the speed. Classification of the driving style is real time and almost cost nothing.