Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning
Abstract This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimiza...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86743-z |
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| author | J. Arun Pandian Ramkumar Thirunavukarasu L. Thanga Mariappan |
| author_facet | J. Arun Pandian Ramkumar Thirunavukarasu L. Thanga Mariappan |
| author_sort | J. Arun Pandian |
| collection | DOAJ |
| description | Abstract This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques. Convolutional Neural Networks (CNNs) architecture which includes ENet, PINet, ResNet-50, ResNet-101, SqueezeNet, and VGG16Net are employed in lane detection problems to construct segmentation models and demonstrate proficiency in distinct road conditions. However, the proposed MAB-Ensemble technique overcomes the limitations of individual models by dynamically selecting the most suitable CNN model based on prevailing environmental factors. The proposed technique optimizes the segmentation accuracy and treats the attained accuracy as a reward signal in the context of reinforcement learning by interacting with the environment through CNN model selection. The MAB-Ensemble achieved an overall accuracy of 90.28% in different road conditions. The results overcome the performance of the individual CNN models and state-of-the-art ensemble techniques. Also, it demonstrates superior performance which includes daytime, night-time, and abnormal road conditions. The MAB-Ensemble technique offers a promising solution for robust lane detection by harnessing the collective strengths of diverse CNN models. |
| format | Article |
| id | doaj-art-2bc70144782946c39255e3a74ee7cefe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2bc70144782946c39255e3a74ee7cefe2025-01-26T12:28:23ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-86743-zEnhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learningJ. Arun Pandian0Ramkumar Thirunavukarasu1L. Thanga Mariappan2School of Computer Science Engineering and Information Systems, Vellore Institute of TechnologySchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologySchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologyAbstract This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques. Convolutional Neural Networks (CNNs) architecture which includes ENet, PINet, ResNet-50, ResNet-101, SqueezeNet, and VGG16Net are employed in lane detection problems to construct segmentation models and demonstrate proficiency in distinct road conditions. However, the proposed MAB-Ensemble technique overcomes the limitations of individual models by dynamically selecting the most suitable CNN model based on prevailing environmental factors. The proposed technique optimizes the segmentation accuracy and treats the attained accuracy as a reward signal in the context of reinforcement learning by interacting with the environment through CNN model selection. The MAB-Ensemble achieved an overall accuracy of 90.28% in different road conditions. The results overcome the performance of the individual CNN models and state-of-the-art ensemble techniques. Also, it demonstrates superior performance which includes daytime, night-time, and abnormal road conditions. The MAB-Ensemble technique offers a promising solution for robust lane detection by harnessing the collective strengths of diverse CNN models.https://doi.org/10.1038/s41598-025-86743-zAutonomous vehiclesConvolutional neural networksEnsemble learningMulti-armed banditThompson sampling |
| spellingShingle | J. Arun Pandian Ramkumar Thirunavukarasu L. Thanga Mariappan Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning Scientific Reports Autonomous vehicles Convolutional neural networks Ensemble learning Multi-armed bandit Thompson sampling |
| title | Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning |
| title_full | Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning |
| title_fullStr | Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning |
| title_full_unstemmed | Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning |
| title_short | Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning |
| title_sort | enhancing lane detection in autonomous vehicles with multi armed bandit ensemble learning |
| topic | Autonomous vehicles Convolutional neural networks Ensemble learning Multi-armed bandit Thompson sampling |
| url | https://doi.org/10.1038/s41598-025-86743-z |
| work_keys_str_mv | AT jarunpandian enhancinglanedetectioninautonomousvehicleswithmultiarmedbanditensemblelearning AT ramkumarthirunavukarasu enhancinglanedetectioninautonomousvehicleswithmultiarmedbanditensemblelearning AT lthangamariappan enhancinglanedetectioninautonomousvehicleswithmultiarmedbanditensemblelearning |