A Driver Assistant System Using Behavior Patterns and Fuzzy Logic to Enhance Safety

Arman Kavoosi Ghafi, Behnam Mirzaeian, Mohammad Hossein Shafiabadi, Adnan Samerian Hosseini, Adel Hojatkhah, Elham Salehi, Helena Kojooyan Jafari


In this article, the purpose of using learning algorithms is to create an advanced system of the driver's assistant in order to provide the driver with the driving safety by considering general and personal driving features.We used the decision tree algorithm to represent such a system. At as the first step, appropriate data are collected. The aim is to select the factors influencing the design of the warning system. In the next step, we prepared the data. Then outlier data is differentiated using categorical algorithms. In the next step, input data is prepared using a fuzzy device. The system is a Mamdani fuzzy system. In the last step, we determined the root, branch, and leaves of the decision tree model using the entropy method. In this paper, the rules are extracted from 5 attributes along with a target variable which has four modes. The rules of the fuzzy system are implemented on a local database and the results are provided. Finally, the results of the designed model are compared with the results of the previous.


Decision tree, Fuzzy logic, Driving learning algorithms, Mamdani fuzzy system, Entropy method

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