RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
Osdar23: Open sensor data for rail 2023,
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A modular system fuses object detection, segmentation, and LiDAR-improved depth estimation to achieve 0.63 m MAE for obstacle distances on synthetic railway data.
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RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation
RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
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Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments
A modular system fuses object detection, segmentation, and LiDAR-improved depth estimation to achieve 0.63 m MAE for obstacle distances on synthetic railway data.