Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion
This paper presents a sophisticated self-driving vehicle (SDV) model that addresses the challenges of navigating complex road networks characterized by high traffic and unpredictable environments. The model integrates state-of-the-art image processing techniques with advanced sensor fusion, utilizin...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10737305/ |
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| author | Kalapraveen Bagadi Naveen Kumar Vaegae Visalakshi Annepu Khaled Rabie Shafiq Ahmad Thokozani Shongwe |
| author_facet | Kalapraveen Bagadi Naveen Kumar Vaegae Visalakshi Annepu Khaled Rabie Shafiq Ahmad Thokozani Shongwe |
| author_sort | Kalapraveen Bagadi |
| collection | DOAJ |
| description | This paper presents a sophisticated self-driving vehicle (SDV) model that addresses the challenges of navigating complex road networks characterized by high traffic and unpredictable environments. The model integrates state-of-the-art image processing techniques with advanced sensor fusion, utilizing EfficientDet D0 and Haar Cascade object detection models to identify obstacles, road signs, and traffic signals accurately. The integration of data from cameras and ultrasonic sensors enables the creation of a precise 2D map of the vehicle’s surroundings, which, combined with a robust decision-making algorithm, allows for optimal performance in challenging traffic scenarios. The SDV prototype was tested extensively in a custom-built artificial environment, where it demonstrated its ability to handle various real-world scenarios, including lane detection, obstacle avoidance, and decision-making in the presence of stationary obstacles and heavy traffic. The experimental results confirm the model’s effectiveness in enhancing SDV capabilities, paving the way for safer and more efficient autonomous transportation systems. It is found from our experiments that the average precision for obstacle detection models is 0.729, the average recall is 0.758, and the prototype’s ability to process at 24 frames per second highlights the efficiency and accuracy of our proposed model. |
| format | Article |
| id | doaj-art-c3f53302eb5a4beb98bc52bc6899262c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c3f53302eb5a4beb98bc52bc6899262c2025-08-20T03:53:17ZengIEEEIEEE Access2169-35362024-01-011218714118715910.1109/ACCESS.2024.348786810737305Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor FusionKalapraveen Bagadi0https://orcid.org/0000-0003-1082-1972Naveen Kumar Vaegae1https://orcid.org/0000-0002-0292-697XVisalakshi Annepu2https://orcid.org/0000-0002-7199-1898Khaled Rabie3https://orcid.org/0000-0003-0043-2025Shafiq Ahmad4https://orcid.org/0000-0003-0712-9133Thokozani Shongwe5https://orcid.org/0000-0002-3011-7600School of Electronics Engineering, VIT-AP University, Amaravati, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, VIT-AP University, Amaravati, IndiaDepartment of Computer Engineering and Center for Communication Systems and Sensing, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaIndustrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi ArabiaElectrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg, South AfricaThis paper presents a sophisticated self-driving vehicle (SDV) model that addresses the challenges of navigating complex road networks characterized by high traffic and unpredictable environments. The model integrates state-of-the-art image processing techniques with advanced sensor fusion, utilizing EfficientDet D0 and Haar Cascade object detection models to identify obstacles, road signs, and traffic signals accurately. The integration of data from cameras and ultrasonic sensors enables the creation of a precise 2D map of the vehicle’s surroundings, which, combined with a robust decision-making algorithm, allows for optimal performance in challenging traffic scenarios. The SDV prototype was tested extensively in a custom-built artificial environment, where it demonstrated its ability to handle various real-world scenarios, including lane detection, obstacle avoidance, and decision-making in the presence of stationary obstacles and heavy traffic. The experimental results confirm the model’s effectiveness in enhancing SDV capabilities, paving the way for safer and more efficient autonomous transportation systems. It is found from our experiments that the average precision for obstacle detection models is 0.729, the average recall is 0.758, and the prototype’s ability to process at 24 frames per second highlights the efficiency and accuracy of our proposed model.https://ieeexplore.ieee.org/document/10737305/ArduinoEfficientDetimage processingobject detectionraspberry Piself-driving vehicles |
| spellingShingle | Kalapraveen Bagadi Naveen Kumar Vaegae Visalakshi Annepu Khaled Rabie Shafiq Ahmad Thokozani Shongwe Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion IEEE Access Arduino EfficientDet image processing object detection raspberry Pi self-driving vehicles |
| title | Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion |
| title_full | Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion |
| title_fullStr | Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion |
| title_full_unstemmed | Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion |
| title_short | Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion |
| title_sort | advanced self driving vehicle model for complex road navigation using integrated image processing and sensor fusion |
| topic | Arduino EfficientDet image processing object detection raspberry Pi self-driving vehicles |
| url | https://ieeexplore.ieee.org/document/10737305/ |
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