Using N-Version Architectures for Railway Segmentation with Deep Neural Networks
Autonomous trains require reliable and accurate environmental perception to take over safety-critical tasks from the driver. This paper investigates the application of N-version architectures to rail track detection using Deep Neural Networks (DNNs) as a means to improve the safety of machine learni...
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MDPI AG
2025-05-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/7/2/49 |
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| author | Philipp Jaß Carsten Thomas |
| author_facet | Philipp Jaß Carsten Thomas |
| author_sort | Philipp Jaß |
| collection | DOAJ |
| description | Autonomous trains require reliable and accurate environmental perception to take over safety-critical tasks from the driver. This paper investigates the application of N-version architectures to rail track detection using Deep Neural Networks (DNNs) as a means to improve the safety of machine learning (ML)-enabled perception systems. We combine three different neural network architectures (WCID, VGG16-UNet, MobileNet–SegNet) in a 3M1I configuration. In this configuration, we apply two fusion methods to increase accuracy and to enable error detection: Maximum Confidence Voting (MCV), combining the DNN predictions at the image level, and Pixel Majority Voting (PMV), a novel approach for combining the predictions at the pixel level. In addition, we implement a new method for evaluating and combining prediction confidence values in the N-version architecture during runtime. We adjust the overall prediction confidence according to the conformity of all individual predictions, which is not possible with an individual network. Our results show that the N-version architecture not only enables a detection of erroneous predictions by utilizing those adjusted confidence values, but it can also partially improve the predictions by using the PMV combination algorithm. This work emphasizes the importance of model diversity and appropriate thresholds for an accurate assessment of prediction safety. These approaches can significantly improve the practical applicability of ML-based systems in safety-critical domains such as rail transportation. |
| format | Article |
| id | doaj-art-c64e5705b4364d12b98f4bb67940ff0d |
| institution | Kabale University |
| issn | 2504-4990 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-c64e5705b4364d12b98f4bb67940ff0d2025-08-20T03:27:30ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902025-05-01724910.3390/make7020049Using N-Version Architectures for Railway Segmentation with Deep Neural NetworksPhilipp Jaß0Carsten Thomas1HTW Berlin, University of Applied Sciences, Wilhelminenhofstraße 75A, 12459 Berlin, GermanyHTW Berlin, University of Applied Sciences, Wilhelminenhofstraße 75A, 12459 Berlin, GermanyAutonomous trains require reliable and accurate environmental perception to take over safety-critical tasks from the driver. This paper investigates the application of N-version architectures to rail track detection using Deep Neural Networks (DNNs) as a means to improve the safety of machine learning (ML)-enabled perception systems. We combine three different neural network architectures (WCID, VGG16-UNet, MobileNet–SegNet) in a 3M1I configuration. In this configuration, we apply two fusion methods to increase accuracy and to enable error detection: Maximum Confidence Voting (MCV), combining the DNN predictions at the image level, and Pixel Majority Voting (PMV), a novel approach for combining the predictions at the pixel level. In addition, we implement a new method for evaluating and combining prediction confidence values in the N-version architecture during runtime. We adjust the overall prediction confidence according to the conformity of all individual predictions, which is not possible with an individual network. Our results show that the N-version architecture not only enables a detection of erroneous predictions by utilizing those adjusted confidence values, but it can also partially improve the predictions by using the PMV combination algorithm. This work emphasizes the importance of model diversity and appropriate thresholds for an accurate assessment of prediction safety. These approaches can significantly improve the practical applicability of ML-based systems in safety-critical domains such as rail transportation.https://www.mdpi.com/2504-4990/7/2/49machine learningN-versionsafetyautonomous rail systemssemantic segmentationsafety-critical AI |
| spellingShingle | Philipp Jaß Carsten Thomas Using N-Version Architectures for Railway Segmentation with Deep Neural Networks Machine Learning and Knowledge Extraction machine learning N-version safety autonomous rail systems semantic segmentation safety-critical AI |
| title | Using N-Version Architectures for Railway Segmentation with Deep Neural Networks |
| title_full | Using N-Version Architectures for Railway Segmentation with Deep Neural Networks |
| title_fullStr | Using N-Version Architectures for Railway Segmentation with Deep Neural Networks |
| title_full_unstemmed | Using N-Version Architectures for Railway Segmentation with Deep Neural Networks |
| title_short | Using N-Version Architectures for Railway Segmentation with Deep Neural Networks |
| title_sort | using n version architectures for railway segmentation with deep neural networks |
| topic | machine learning N-version safety autonomous rail systems semantic segmentation safety-critical AI |
| url | https://www.mdpi.com/2504-4990/7/2/49 |
| work_keys_str_mv | AT philippjaß usingnversionarchitecturesforrailwaysegmentationwithdeepneuralnetworks AT carstenthomas usingnversionarchitecturesforrailwaysegmentationwithdeepneuralnetworks |