Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition. (October 2018)

• Record Type: Journal Article
• Authors: Eftekhari, Hamid Reza; Ghatee, Mehdi
• Is Part Of: Transportation research. Volume 58(2018)
• Page Start: 782
• Page End: 796
• Publication Date: 2018-10
• Subjects: Driver behavior evaluation -- Wavelet transform -- Aggressive driving -- Smartphone sensors -- ANFIS -- Decision fusion -- Pattern recognition; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2018.06.044
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Recognizing the overall driving behavior by smartphone with 92% accuracy. Defining four new features as time-series from IMU sensors. Decomposing the features by Discrete Wavelet Transform (DTW). Using Adaptive Neuro Fuzzy Inference System (ANFIS) on the energy of the extracted features from DTW. Classification the overall driving behaviors based on DAS questionnaire by decision fusion on ANFISs outputs. Abstract: Monitoring and evaluating of driving behavior is the main goal of this paper that encourage us to develop a new system based on Inertial Measurement Unit (IMU) sensors of smartphones. In this system, a hybrid of Discrete Wavelet Transformation (DWT) and Adaptive Neuro Fuzzy Inference System (ANFIS) is used to recognize overall driving behaviors. The behaviors are classified into the safe, the semi-aggressive, and the aggressive classes that are adopted with Driver Anger Scale (DAS) self-reported questionnaire results. The proposed system extracts four features from IMU sensors in the forms of time series. They are decomposed by DWT in two levels and their energies are sent to six ANFISs. Each ANFIS models the different perception about driving behavior under uncertain knowledge and returns the similarity or dissimilarity between driving behaviors. The results of these six ANFISs are combined by three different decision fusion approaches. Results show that Coiflet-2 is the most suitable mother wavelet for driving behavior analysis. In addition, the proposed system recognizes the overall driving behavior patterns with 92% accuracy without necessity to evaluate the maneuvers one by one. We show that without longitude acceleration data, the driver behavior cannot be recognized successfully while the results do not disturb substantially when the gyroscope is not available.