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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.01974 |
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| _version_ | 1866911416342020096 |
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| author | Yan, Chenyang Bengtsson, Mats |
| author_facet | Yan, Chenyang Bengtsson, Mats |
| contents | Level crossing accidents remain a significant safety concern in modern railway systems, particularly under adverse weather conditions that degrade sensor performance. This review surveys state-of-the-art sensor technologies and fusion strategies for obstacle detection at railway level crossings, with a focus on robustness, detection accuracy, and environmental resilience. Individual sensors such as inductive loops, cameras, radar, and LiDAR offer complementary strengths but involve trade-offs, including material dependence, reduced visibility, and limited resolution in harsh environments. We analyze each modality's working principles, weather-induced vulnerabilities, and mitigation strategies, including signal enhancement and machine-learning-based denoising. We further review multi-sensor fusion approaches, categorized as data-level, feature-level, and decision-level architectures, that integrate complementary information to improve reliability and fault tolerance. The survey concludes with future research directions, including adaptive fusion algorithms, real-time processing pipelines, and weather-resilient datasets to support the deployment of intelligent, fail-safe detection systems for railway safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01974 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Obstacle Detection at Level Crossings under Adverse Weather Conditions -- A Survey Yan, Chenyang Bengtsson, Mats Signal Processing Level crossing accidents remain a significant safety concern in modern railway systems, particularly under adverse weather conditions that degrade sensor performance. This review surveys state-of-the-art sensor technologies and fusion strategies for obstacle detection at railway level crossings, with a focus on robustness, detection accuracy, and environmental resilience. Individual sensors such as inductive loops, cameras, radar, and LiDAR offer complementary strengths but involve trade-offs, including material dependence, reduced visibility, and limited resolution in harsh environments. We analyze each modality's working principles, weather-induced vulnerabilities, and mitigation strategies, including signal enhancement and machine-learning-based denoising. We further review multi-sensor fusion approaches, categorized as data-level, feature-level, and decision-level architectures, that integrate complementary information to improve reliability and fault tolerance. The survey concludes with future research directions, including adaptive fusion algorithms, real-time processing pipelines, and weather-resilient datasets to support the deployment of intelligent, fail-safe detection systems for railway safety. |
| title | Obstacle Detection at Level Crossings under Adverse Weather Conditions -- A Survey |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.01974 |