An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance
Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Becau...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/8548172 |
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| author | Gunapriya Balan Singaravelan Arumugam Suresh Muthusamy Hitesh Panchal Hossam Kotb Mohit Bajaj Sherif S. M. Ghoneim null Kitmo |
| author_facet | Gunapriya Balan Singaravelan Arumugam Suresh Muthusamy Hitesh Panchal Hossam Kotb Mohit Bajaj Sherif S. M. Ghoneim null Kitmo |
| author_sort | Gunapriya Balan |
| collection | DOAJ |
| description | Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers’ different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy. |
| format | Article |
| id | doaj-art-7217f8f845c94f4582b2cb17bcc7911b |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-7217f8f845c94f4582b2cb17bcc7911b2025-02-03T01:00:43ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/8548172An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better PerformanceGunapriya Balan0Singaravelan Arumugam1Suresh Muthusamy2Hitesh Panchal3Hossam Kotb4Mohit Bajaj5Sherif S. M. Ghoneim6null Kitmo7Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electronics and Communication EngineeringDepartment of Mechanical EngineeringDepartment of Electrical Power and MachinesDepartment of Electrical EngineeringElectrical Engineering DepartmentDepartment of Renewable EnergyTechnology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers’ different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy.http://dx.doi.org/10.1155/2022/8548172 |
| spellingShingle | Gunapriya Balan Singaravelan Arumugam Suresh Muthusamy Hitesh Panchal Hossam Kotb Mohit Bajaj Sherif S. M. Ghoneim null Kitmo An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance International Transactions on Electrical Energy Systems |
| title | An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance |
| title_full | An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance |
| title_fullStr | An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance |
| title_full_unstemmed | An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance |
| title_short | An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance |
| title_sort | improved deep learning based technique for driver detection and driver assistance in electric vehicles with better performance |
| url | http://dx.doi.org/10.1155/2022/8548172 |
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