23 May 2016 |
Research article |
Software Systems, Multimedia and Cybersecurity
How to Characterize Driving Behaviors: Classification Methods
In the first part of this article, entitled How to Characterize Driving Behaviors: Insurance Plans, we explained why vehicle tracking is one of the significant concerns for insurance and vehicle rental companies. Several models of insurance coverage now proposed by insurance companies were explained. The second part of this article will present the different methods to classify driving behaviors, the research study and the major conclusions.
Classification Methods Used in Driving Behaviors
There are three important methods that contribute to the classification of driving behaviors: measuring the brain activity of the driver, measuring the physical characteristics of the driver (physical and facial expression detection methods) and measuring the dynamics of the vehicle. Associated with the latter method, artificial intelligent techniques were developed to analyze driving behavior monitoring. These non-linear techniques based on machine learning methods are widely used in monitoring driving behaviors. These techniques are summarized as follows:
1. Artificial Neural Networks (ANN)
Artificial Neural Network (ANN) algorithms are commonly used to observe the statistical modeling of driving behaviors [7, 8]. There are some significant advantages of the ANN in monitoring driving behaviors, such as 1) allowing the pattern extraction without knowing of the relation between inputs and outputs, 2) less demand for formal training, and 3) recognition of all probable interactions between the predictor variables. However, the black box nature and the complex computation of the ANN are the two main drawbacks of these algorithms [9, 10].
2. Support Vector Machines (SVM)
Support Vector Machines (SVM) are capable of computing the different emotional states of the driver using effective nonlinear methods . Additionally, the SVM are employed for the purpose of pattern categorization, linear or nonlinear relationships between Input-Output (I/O) and object detection .
3. Hidden Markov Models (HMM)
Hidden Markov Models (HMM) are used for the identification of driving states monitoring the automotive vehicle . To achieve this goal, using the Baum-Welch re-estimation method is considered in many situations .
4. Fuzzy Inference Systems (FIS)
Fuzzy Inference Systems (FIS) are a rule-based expert method able to mimic human thinking and linguistic concepts as opposed to the typical logic systems. FIS are proper methods where: 1. Process of analysis is complex and time-consuming with conventional methods; 2. Available raw measurements are interpreted approximately or inaccurately. The two major types of FIS are Mamdani and Sugeno-TSK recent literature focused on the comparison of these two methods .
In the research paper, we evaluate the two major types of FIS: Mamdani and Sugeno-TSK. They will be analysed and compared to find out which is the best one in characterizing driving behaviors. We will propose a new FIS model for characterizing driving behaviors based on this study.
The diagram of the proposed methodology is shown in Figure 4. First, the driver actions are acquired using the inertial navigation systems (INS), Global Navigation Satellite Systems (GNSS), and On-Board Diagnostic (OBD) systems of the vehicle. Later, the features of the driver actions will be applied to recognize the most likely driving behavior by the fuzzy controller. Finally, the outputs of this controller will be utilized to estimate driver behavior and performance.The proposed FIS algorithm is modeled in Matlab and Simulink to evaluate the algorithm in two different types [13, 14]. First, the Mamdani-type of system is evaluated. Second, this model is evaluated by the Sugeno-TSK type. Finally, the result of the proposed model with the two FIS types will be compared accurately. The proposed FIS model consists of seven inputs and two FIS outputs. The inputs are determined by the different parameters as shown in Figure 5.
The proposed solution is based on an advanced model of driving behaviors, in order to identify the driving quality using two popular Fuzzy Inference Systems (FIS). The results confirm that higher accuracy and high dynamic behaviors can be achieved, using the Sugeno-TSK type compared to the Mamdani type. The high cross-correlation values of the two FIS types validate the stability and reliability of the adopted FIS types in estimating the driving behavior with no unusual exception in the results.
Research Projects at the Lassena Laboratory
Neda Navidi is a PH.D. Student at ÉTS, doing a research on novel metrics for monitoring of driving behavior based on the intelligent navigator with ultra-low-cost INS/GNSS integration.
Program : Electrical Engineering
Research laboratories : LASSENA – Laboratory of Space Technologies, Embedded Systems, Navigation and Avionic
René Jr Landry
René Jr Landry is a professor in the Electrical Engineering Department at ÉTS and the Director of LASSENA. His expertise in embedded systems, navigation, and avionics applies notably in transportation, aeronautics and space technologies.
Program : Electrical Engineering