Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability
In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunicat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/7/3423 |
| _version_ | 1797607020605472768 |
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| author | K. Suganthi M. Arun Kumar N. Harish S. HariKrishnan G. Rajesh S. Sofana Reka |
| author_facet | K. Suganthi M. Arun Kumar N. Harish S. HariKrishnan G. Rajesh S. Sofana Reka |
| author_sort | K. Suganthi |
| collection | DOAJ |
| description | In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work. |
| first_indexed | 2024-03-11T05:25:26Z |
| format | Article |
| id | doaj.art-09d8dd6119f24b218fa620b4cb74a4c3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:25:26Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-09d8dd6119f24b218fa620b4cb74a4c32023-11-17T17:32:20ZengMDPI AGSensors1424-82202023-03-01237342310.3390/s23073423Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic DrivabilityK. Suganthi0M. Arun Kumar1N. Harish2S. HariKrishnan3G. Rajesh4S. Sofana Reka5School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, IndiaDepartment of Information Technology, MIT Campus, Anna University, Chennai 600025, IndiaCentre for Smart Grid Technologies, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, IndiaIn conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work.https://www.mdpi.com/1424-8220/23/7/3423advanced driver assistance system designinternet of things (IoT)data analyticsover-the-airvehicle communicationmachine learning |
| spellingShingle | K. Suganthi M. Arun Kumar N. Harish S. HariKrishnan G. Rajesh S. Sofana Reka Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability Sensors advanced driver assistance system design internet of things (IoT) data analytics over-the-air vehicle communication machine learning |
| title | Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability |
| title_full | Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability |
| title_fullStr | Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability |
| title_full_unstemmed | Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability |
| title_short | Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability |
| title_sort | advanced driver assistance system based on iot v2v and v2i for vision enabled lane changing with futuristic drivability |
| topic | advanced driver assistance system design internet of things (IoT) data analytics over-the-air vehicle communication machine learning |
| url | https://www.mdpi.com/1424-8220/23/7/3423 |
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