A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System
Accurate road surface parameter identification is considered essential for selecting the appropriate controlling threshold in the Anti-lock Braking System (ABS) utilized in modern vehicles. This paper presents a data-based method for road surface parameter estimation. The proposed method utilizes a...
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| Format: | Article |
| Language: | English |
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University of Diyala
2022-12-01
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| Series: | Diyala Journal of Engineering Sciences |
| Subjects: | |
| Online Access: | https://djes.info/index.php/djes/article/view/1030 |
| _version_ | 1798004744462008320 |
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| author | Ayad qays Abdulrahim Thiab Humod Oday Ali Ahmed |
| author_facet | Ayad qays Abdulrahim Thiab Humod Oday Ali Ahmed |
| author_sort | Ayad qays |
| collection | DOAJ |
| description |
Accurate road surface parameter identification is considered essential for selecting the appropriate controlling threshold in the Anti-lock Braking System (ABS) utilized in modern vehicles. This paper presents a data-based method for road surface parameter estimation. The proposed method utilizes a pattern recognition technique that works to estimate the road type during braking. A detailed analysis and related comparison is provided for several pattern recognition techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT), which were chosen among previously studied pattern recognition techniques. A model for the ABS system is implemented with MATLAB Simulink, and the required data is extracted to be utilized to train each model individually. After training is complete, a test has been applied in order to obtain the performance of each trained model. In particular, accuracy and sensitivity are utilized to compare the effectiveness of these models, with 96% for the SVM, 95.2% for the DT model, and 94% for the KNN model. Although the SVM classifier accuracy was better than both the KNN and DT classifiers, all classifiers presented a high performance accuracy that proves the possibility of utilizing a data-based method for road surface parameter identification that increases the reliability of safety systems like the ABS.
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| first_indexed | 2024-04-11T12:29:24Z |
| format | Article |
| id | doaj.art-b3b064a26ca8461ca9082d6021fac8e1 |
| institution | Directory Open Access Journal |
| issn | 1999-8716 2616-6909 |
| language | English |
| last_indexed | 2024-04-11T12:29:24Z |
| publishDate | 2022-12-01 |
| publisher | University of Diyala |
| record_format | Article |
| series | Diyala Journal of Engineering Sciences |
| spelling | doaj.art-b3b064a26ca8461ca9082d6021fac8e12022-12-22T04:23:50ZengUniversity of DiyalaDiyala Journal of Engineering Sciences1999-87162616-69092022-12-0115410.24237/djes.2022.15411A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking SystemAyad qays0Abdulrahim Thiab Humod1Oday Ali Ahmed2Department of Electrical Engineering, University of Technology,IraqDepartment of Electrical Engineering, University of Technology, Iraq.Department of Electrical Engineering, University of Technology, Iraq. Accurate road surface parameter identification is considered essential for selecting the appropriate controlling threshold in the Anti-lock Braking System (ABS) utilized in modern vehicles. This paper presents a data-based method for road surface parameter estimation. The proposed method utilizes a pattern recognition technique that works to estimate the road type during braking. A detailed analysis and related comparison is provided for several pattern recognition techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT), which were chosen among previously studied pattern recognition techniques. A model for the ABS system is implemented with MATLAB Simulink, and the required data is extracted to be utilized to train each model individually. After training is complete, a test has been applied in order to obtain the performance of each trained model. In particular, accuracy and sensitivity are utilized to compare the effectiveness of these models, with 96% for the SVM, 95.2% for the DT model, and 94% for the KNN model. Although the SVM classifier accuracy was better than both the KNN and DT classifiers, all classifiers presented a high performance accuracy that proves the possibility of utilizing a data-based method for road surface parameter identification that increases the reliability of safety systems like the ABS. https://djes.info/index.php/djes/article/view/1030Anti-lock Braking SystemBurkhardt tire modelk-nearest neighborSupport Vector MachineDecision Tree |
| spellingShingle | Ayad qays Abdulrahim Thiab Humod Oday Ali Ahmed A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System Diyala Journal of Engineering Sciences Anti-lock Braking System Burkhardt tire model k-nearest neighbor Support Vector Machine Decision Tree |
| title | A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System |
| title_full | A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System |
| title_fullStr | A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System |
| title_full_unstemmed | A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System |
| title_short | A Data Based Method Road Surface Parameters Estimation for Anti-Lock Braking System |
| title_sort | data based method road surface parameters estimation for anti lock braking system |
| topic | Anti-lock Braking System Burkhardt tire model k-nearest neighbor Support Vector Machine Decision Tree |
| url | https://djes.info/index.php/djes/article/view/1030 |
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