publications.bib

@ARTICLE{iglesias2026interpretable,
  author={Aitor Iglesias and Jose Luis Apellaniz and Nerea Aranjuelo and Pedro Brandimarte and Jokin Irastorza and Marcos Nieto},
  journal={IEEE Sensors Journal}, 
  title={Interpretable Railway Track and Obstacle Detection using On-board LiDAR}, 
  year={2026},
  volume={},
  number={},
  pages={1-1},
  abstract={Real-time obstacle detection on railway tracks is crucial for preventing accidents. Current solutions typically rely on static sensors monitoring limited zones and black-box methods with unpredictable behaviour for different scenarios. In our work, we present an interpretable and real-time railway track and obstacle detection method using an on-board LiDAR (Light Detection And Ranging) sensor. Our method comprises two main steps: point cloud-based railway track detection and obstacle detection for the predicted trajectory. To estimate railway tracks we define a pillar max height-based filtering, followed by a point aggregation operation and a polynomial curve fitting. For the given railway tracks, we base our object detection on point clustering. Both railway track and obstacle detections are tracked in time for more robust and smooth results. To address the lack of available datasets, we create a synthetic dataset for evaluating obstacle detection. We evaluate our method on both real and synthetic data, and the results demonstrate its effectiveness in accurately detecting railway tracks and objects.},
  keywords={Tracking,Rail transportation,Signal detection,Clouds,Image sensors,Rails,Laser radar,Distance measurement,Timing,Meters,LiDAR,railway track detection,obstacle detection,point cloud processing,synthetic datasets,railway safety},
  doi={10.1109/JSEN.2026.3730546},
  ISSN={1558-1748},
  note={Early Access},
  url={https://ieeexplore.ieee.org/document/11686188},
  }