Obstacle Detection at Level Crossings under Adverse Weather Conditions -- A Survey

Chenyang Yan; Mats Bengtsson

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arXiv · 2602.01974

Obstacle Detection at Level Crossings under Adverse Weather Conditions -- A Survey

Chenyang Yan·Mats Bengtsson

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Abstract

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.

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Chenyang Yan, Mats Bengtsson. 2026-02-02. Obstacle Detection at Level Crossings under Adverse Weather Conditions -- A Survey. https://arxiv.org/abs/2602.01974

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