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

Full Text:

Abstract PDF


Chen, F., & Wang, L., & Jiang, B., & Wen, CH. (2014). A novel hybrid petri net model for urban intersection and its application in signal control strategy: Elsevier Journal of the Franklin institute, 4357-4380.

Cheng, S., & Mu, Q., & Zhang, H., & Zhang, Y. (2014). A Fuzzy Decision Tree Model for Airport Terminal, Departure Passenger Traffic Forecasting. In CICTP safe, Smart, and Sustainable Multimodal Transportation Systems, 11-17.

El Faouzi, N.-E., Leung, H., & Kurian, A. (2011). Data fusion in intelligent transportation systems: Progress and challenges–A survey. Information Fusion, 12(1), 4-10.

Kheirandish, A., Akbari, E., Nilashi, M., & Dahari, M. (2019). Using ANFIS technique for PEM fuel cell electric bicycle prediction model. International Journal of Environmental Science and Technology, 1-8.

Manley, E., & Cheng, T., & Penn, A., & Emmonds, A. (2014). A framework for simulating large-scale complex urban traffic dynamics through hybrid agent-based modelling: Elsevier,Computers, Environment and urban systems, 27-36.

Nilashi, M., Ahani, A., Esfahani, M. D., Yadegaridehkordi, E., Samad, S., Ibrahim, O., ... & Akbari, E. (2019). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 215, 767-783.

Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 12(1), 13-20.

Nilashi, M., Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics and Informatics, 34(4), 133-144.

Nilashi, M., Ibrahim, O., Ahmadi, H., Shahmoradi, L., & Farahmand, M. (2018a). A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques. Biocybernetics and Biomedical Engineering, 38(1), 1-15.

Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., & Alizadeh, A. (2018b). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 28, 168-179.

Panchev, C., & Dobrev, P., & Nicholson, J. (2014). Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees: On Engineering Applications of Neural Networks, 175-182.

Placzek, B. (2014). A self-organizing system for urban traffic control based on the predictive interval microscopic model: Engineering applications of artificial intelligence, 75-84.

Rafore, M. (2014). simulating the driving risk in North highway depends on the environment and traffic factors: a data mining approach. Anales de Pediatría (English Edition), 81-109.

Rahbari, D. (2014). Help the genetic algorithm to minimize the urban traffic on intersections: International journal of research in computer science, 4(4) 9-19.

Xu, Y., & Song, X., & Weng, Zh., & Gray, T. (2014). An entry time-based supply framework (ETSF) for microscopic traffic simulations: Elsevier, simulation modeling practice and theory, 185-195.

Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199-210.

Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624-1639.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.