An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization
The increasing trend of autonomous driving vehicles in smart cities emphasizes the need for safe travel. However, the presence of obstacles, potholes, and complex road environments, such as poor illumination and occlusion, can cause blurred road images that may impact the accuracy of maneuver predic...
| প্রধান লেখক: | , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
| প্রকাশিত: |
Universitas Ahmad Dahlan
2023-07-01
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| মালা: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | http://ijain.org/index.php/IJAIN/article/view/1048 |
| _version_ | 1827793619743932416 |
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| author | Swati Jaiswal Chandra Mohan Balasubramanian |
| author_facet | Swati Jaiswal Chandra Mohan Balasubramanian |
| author_sort | Swati Jaiswal |
| collection | DOAJ |
| description | The increasing trend of autonomous driving vehicles in smart cities emphasizes the need for safe travel. However, the presence of obstacles, potholes, and complex road environments, such as poor illumination and occlusion, can cause blurred road images that may impact the accuracy of maneuver prediction in visual perception systems. To address these challenges, a novel ensemble model named ABHO-based deep CNN-BiLSTM has been proposed for traffic sign detection. This model combines a hybrid convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the alarming-based hunting optimization (ABHO) algorithm to improve maneuver prediction accuracy. Additionally, a modified hough-enabled lane generative adversarial network (ABHO based HoughGAN) has been proposed, which is designed to be robust to blurred images. The ABHO algorithm, inspired by the defending and social characteristics of starling birds and Canis kojot, allows the model to efficiently search for the optimal solution from the available solutions in the search space. The proposed ensemble model has shown significantly improved accuracy, sensitivity, and specificity in maneuver prediction compared to previously utilized methods, with minimal error during lane detection. Overall, the proposed ensemble model addresses the challenges faced by autonomous driving vehicles in complex and obstructed road environments, offering a promising solution for enhancing safety and reliability in smart cities. |
| first_indexed | 2024-03-11T18:22:05Z |
| format | Article |
| id | doaj.art-5d2d1c0e5a7842998b4e2d33f39d68b9 |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-03-11T18:22:05Z |
| publishDate | 2023-07-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-5d2d1c0e5a7842998b4e2d33f39d68b92023-10-15T04:42:26ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612023-07-019230131810.26555/ijain.v9i2.1048251An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimizationSwati Jaiswal0Chandra Mohan Balasubramanian1School of Computer Science and Engineering, Vellore Institute of Technology, Tamil NaduSchool of Computer Science and Engineering, Vellore Institute of Technology, Tamil NaduThe increasing trend of autonomous driving vehicles in smart cities emphasizes the need for safe travel. However, the presence of obstacles, potholes, and complex road environments, such as poor illumination and occlusion, can cause blurred road images that may impact the accuracy of maneuver prediction in visual perception systems. To address these challenges, a novel ensemble model named ABHO-based deep CNN-BiLSTM has been proposed for traffic sign detection. This model combines a hybrid convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the alarming-based hunting optimization (ABHO) algorithm to improve maneuver prediction accuracy. Additionally, a modified hough-enabled lane generative adversarial network (ABHO based HoughGAN) has been proposed, which is designed to be robust to blurred images. The ABHO algorithm, inspired by the defending and social characteristics of starling birds and Canis kojot, allows the model to efficiently search for the optimal solution from the available solutions in the search space. The proposed ensemble model has shown significantly improved accuracy, sensitivity, and specificity in maneuver prediction compared to previously utilized methods, with minimal error during lane detection. Overall, the proposed ensemble model addresses the challenges faced by autonomous driving vehicles in complex and obstructed road environments, offering a promising solution for enhancing safety and reliability in smart cities.http://ijain.org/index.php/IJAIN/article/view/1048deep learningautonomous vehicle drivingtraffic sign detectionlane predictioncontroller optimization |
| spellingShingle | Swati Jaiswal Chandra Mohan Balasubramanian An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization IJAIN (International Journal of Advances in Intelligent Informatics) deep learning autonomous vehicle driving traffic sign detection lane prediction controller optimization |
| title | An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization |
| title_full | An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization |
| title_fullStr | An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization |
| title_full_unstemmed | An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization |
| title_short | An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization |
| title_sort | advanced deep learning model for maneuver prediction in real time systems using alarming based hunting optimization |
| topic | deep learning autonomous vehicle driving traffic sign detection lane prediction controller optimization |
| url | http://ijain.org/index.php/IJAIN/article/view/1048 |
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