A Fuzzy Logic Approach on the Evaluation of Driving Styles and Investigation of Drivability Calibration Effects

Year 2022, Volume: 35 Issue: 2, 668 - 680, 01.06.2022

Samet Aksıt , Akif Yavuz , Osman Taha Şen

https://doi.org/10.35378/gujs.862867

Cited By: 1

Abstract

Increased customer expectations lead the automobile manufacturers to develop innovative solutions, such as mode selection functions that provide different performance and comfort settings for the drivers. Almost all brands have different types of driving modes installed on their vehicles, such as sport mode, economy mode, off-road mode, etc. In the current technology, the mode selection is manually done by the driver. Thus, no effort is taken to match the driver style with available driving modes. However, driving mode selection should be done through an intelligent system such as vehicle control unit, in order to optimize customer expectations related to vehicle performance, driving comfort, and fuel consumption. This can be achieved by the analysis of all drivability maneuvers during any driving cycle. Based on the results of these analyses, drivability calibration settings of the vehicle can be adjusted depending on driver behaviors. In addition, fuel consumption can be improved using suitable calibration for each driver type. In this study, an experimental investigation is carried out in which vehicle data is collected for eleven different drivers at three different drivability calibrations. Furthermore, fuzzy logic algorithms are utilized in order to distinguish the driver characteristics. First, data from nine drivers are used in order to train the fuzzy logic approach. Then, the trained fuzzy logic scheme is used to assess the characteristics of two other drivers, who were left out in the training data set. Hence, it is aimed to obtain an intelligent prediction procedure that can estimate the characteristics of a driver based on their driving styles.

Keywords

Drivability calibration, Fuzzy logic algorithm, Driver classification

References

• [1] Murphey, Y.L., Milton, R., Kiliaris, L., “Driver’s style classification using jerk analysis”, IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, Nashville, TN, USA, (2009).
• [2] Sun, B., Deng, W., Wu, J., Li, Y., Zhu, B., Wu, L., “Research on the classification and identification of driver’s driving style”, 10th International Symposium on Computational Intelligence and Design, Hangzhou, China, 28-32, (2017).
• [3] Lopez, J.O., Pinilla, A.C.C., “Driver Behavior Classification Model based on an Intelligent Driving Diagnosis System”, 15th International IEEE Conference on Intelligent Transportation System, Anchorage, AK, USA, 894-899, (2012).
• [4] Sundbom, M., Falcone, P., Sjöberg, J., “Online Driver Behavior Classification Using Probabilistic ARX Models”, 16th International IEEE Annual Conference on Intelligent Transportation Systems, The Hague, Netherlands, 1107-1112, (2013).
• [5] Ciceo, S., Mollet, Y. Sarrazin, M., Gyselinck, J., Van der Auwarer, H., Martiş, C., “Model-Based Design and Testing for Electric Vehicle Driveability Analysis”, 16th International Conference on Environment and Electrical Engineering, Florence, Italy, 1-4, (2016).
• [6] Ping, P., Qin, W., Xu, Y., Miyajima, C., Takeda, K., “Impact of Driver Behavior on Fuel Consumption: Classification, Evaluation and Prediction Using Machine Learning”, IEEE Access, 7: 78515-78532, (2019).
• [7] Zfnebi, K., Soussi, N., Tikito, K., “Driver Behavior Quantitative Models: Identification and Classification of Variables”, International Symposium on Networks, Computers and Communications, Marrakech, Morocco, 1-6, (2017).
• [8] Zheng, Y., Chase, R.T., Elefteriadou, L., Sisiopiku, V., Schroeder, B., “Driver Types and Their Behaviors Within a High Level of Pedestrian Activity Environment”, Transportation Letters, 9(1): 1-11, (2017).
• [9] Bernardi, M.L., Cimitile, M., Martinelli, F., Mercaldo, F., “Driver Identification: a Time Series Classification Approach”, International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, 1-7, (2018).
• [10] Zhang, D., “Vehicle Parameters Estimation and Driver Behavior Classification for Adaptive Shift Strategy of Heavy Duty Vehicles”, Ph.D. Thesis, Clemson University, SC, USA, (2017).
• [11] Langari, R., Won, J.S., “Intelligent Energy Management Agent for a Parallel Hybrid Vehicle Part I: System Architecture and Design Identification”, IEEE Transactions on Vehicular Technology, 54(3): 925-934, (2005).
• [12] Cheng, Z.J., Jeng, L.W., Li, K., “Behavioral Classification of Drivers for Driving Efficiency Related ADAS Using Artificial Neural Network”, International Conference on Advanced Manufacturing, Yunlin, Taiwan, 173-176, (2018).
• [13] Fernandez, S., Ito, T., “Driver Classification for Intelligent Transportation Systems using Fuzzy Logic”, 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, 1212-1216, (2016).
• [14] Cheung, E., Bera, A., Manocha, D., “Efficient and Safe Vehicle Navigation Based on Driver Behavior Classification”, Conference on Computer Vision and Pattern Recognition Workshop, Salt Lake City, UT, USA, 1024-1031, (2018).
• [15] Lin, N., Zong, C., Tomizuka, M., Song, P., Zhang, Z., Li, G., “An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control”, Mathematical Problems in Engineering, 2014: 1-15, (2014).