Level Crossing

Level crossing research encompasses the analysis and prediction of events where two entities intersect, ranging from pedestrians crossing roads to trains traversing highway crossings. Current research focuses on improving safety and efficiency through computer vision and machine learning, employing models like YOLO variants, UNet architectures, and graph neural networks to detect and predict crossing behavior in diverse scenarios. These advancements are crucial for enhancing safety in transportation systems, particularly for vulnerable road users, and for optimizing traffic flow in autonomous vehicle environments. The development of robust and accurate predictive models is driving progress in both theoretical understanding and practical applications.