A grade crossing is a crossing of a railway line and a motor road. In 2009 alone there were 248 deaths and 682 injuries at grade crossings in the United States. Factors like the elevation profile of a crossing or the environment and foliage around the crossing can render it unsafe. Often, vehicles with low ground clearance bottom out on a crossing with a humped elevation profile. Excessive foliage around the crossing can obstruct the visibility of an approaching train, reducing the time a driver has to stop. Hence ensuring safety requires regular monitoring and timely maintenance of grade crossings across the country. In this paper, we describe our method for automatically inspecting grade crossings. Our work employs principled machine learning methods to detect grade crossings from sensor data and then reconstructs the profile of that rail-road intersection. We then show how traffic simulation on the reconstructed profile can be used to determine whether the crossing is unsafe.
@inproceedings{ranganathan2010, TITLE = {Automated Safety Inspection of Grade Crossings}, AUTHOR = {Pradeep Ranganathan and Edwin Olson}, BOOKTITLE = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems {(IROS)}}, YEAR = {2010}, MONTH = {October}, VOLUME = {}, NUMBER = {}, PAGES = {}, KEYWORDS = {pattern recognition, point cloud, 3D processing, safety analysis, simulation, robot perception}, ISSN = { }, }