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

TL;DR

This survey reviews sensor technologies and fusion strategies for obstacle detection at railway level crossings, emphasizing robustness under adverse weather to enhance safety and reliability.

Contribution

It provides a comprehensive analysis of sensor modalities, fusion architectures, and mitigation techniques, highlighting future research directions for weather-resilient obstacle detection systems.

Findings

Inductive loops, cameras, radar, and LiDAR each have unique strengths and vulnerabilities.

Multi-sensor fusion improves detection reliability and fault tolerance.

Future directions include adaptive algorithms and weather-resilient datasets.

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.