Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion
Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple con...
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2025-03-01
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| author | Susana Dias Pedro J. S. C. P. Sousa João Nunes Francisco Afonso Nuno Viriato Paulo J. Tavares Pedro M. G. P. Moreira |
| author_facet | Susana Dias Pedro J. S. C. P. Sousa João Nunes Francisco Afonso Nuno Viriato Paulo J. Tavares Pedro M. G. P. Moreira |
| author_sort | Susana Dias |
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| description | Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train’s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leixões line. This real-world testing scenario allowed the evaluation of the framework’s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-8eeb406f99c74acb8f752127b6c2f4e82025-08-20T03:43:26ZengMDPI AGApplied Sciences2076-34172025-03-01156311810.3390/app15063118Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data FusionSusana Dias0Pedro J. S. C. P. Sousa1João Nunes2Francisco Afonso3Nuno Viriato4Paulo J. Tavares5Pedro M. G. P. Moreira6INEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalINEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalINEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalINEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalINEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalINEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalINEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Rua Dr. Roberto Frias Nº 400, 4200-465 Porto, PortugalRail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train’s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leixões line. This real-world testing scenario allowed the evaluation of the framework’s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks.https://www.mdpi.com/2076-3417/15/6/3118LiDARobstacle detectionrailwayclustering algorithm |
| spellingShingle | Susana Dias Pedro J. S. C. P. Sousa João Nunes Francisco Afonso Nuno Viriato Paulo J. Tavares Pedro M. G. P. Moreira Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion Applied Sciences LiDAR obstacle detection railway clustering algorithm |
| title | Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion |
| title_full | Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion |
| title_fullStr | Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion |
| title_full_unstemmed | Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion |
| title_short | Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion |
| title_sort | deterministic light detection and ranging lidar based obstacle detection in railways using data fusion |
| topic | LiDAR obstacle detection railway clustering algorithm |
| url | https://www.mdpi.com/2076-3417/15/6/3118 |
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