Automated operation of railroad trains

Abstract

A new over lay technology for the Positive Train Control and Energy Management systems used by the railroad industry today which allows for automated train handling responses to potential on-track hazards. Various sensors, including image-capture devices, radar, and drones, are placed on or proximate to a train. These sensors are used to interface with or override the Positive Train Control and Energy Management systems where those systems activate specific actions such as slowing or stopping a train but hazardous conditions on the track may dictate an alternative response.

What is claimed is: 
     
         1 . A method for autonomously detecting onboard a train a potential hazard condition on a railroad track, the method comprising:
 obtaining a position of the train on the track;   accessing baseline navigational data for each of a plurality of regions of track in advance of the position of the train, the baseline navigational data for each of the plurality of regions including coordinates defining the region and at least one historical pixel calculus value calculated based on pixel values of historical image data corresponding to the region, the historical pixel calculus values in the baseline navigational data having been calculated prior to a current trip of the train;   accessing a current image of the plurality of regions of track in advance of the position of the train;   justifying the current image;   calculating at least one current pixel calculus value for each of the plurality of regions based on pixel values of the justified current image corresponding to the region;   performing, for each of the plurality of regions, a comparison of a tolerance value to a difference between the at least one current pixel calculus value and the at least one historical pixel calculus value for the region.   determining a potential hazard condition based on at least one of the comparisons.   
     
     
         2 . The method of  claim 1 , wherein the historical pixel calculus values are calculated by performing a weighted summation of the pixel values of the historical image data corresponding to the region, and wherein the current pixel calculus values are calculated by performing a weighted summation of pixel values of the justified current image corresponding to the region. 
     
     
         3 . The method of  claim 2 ,
 wherein the baseline navigational data for at least one of the regions further includes definitions for a plurality of grids that divide the region and historical pixel calculus values for each of the plurality of grids based on pixel values of a portion of historical image data corresponding to a respective grid; and   wherein calculating the at least one current pixel calculus value for the at least one of the regions comprises calculating current pixel calculus values for each of the plurality of grids based on pixel values of a portion of the justified current image data corresponding to a respective grid.   
     
     
         4 . The method of  claim 2 , wherein justifying the current image comprises:
 accessing, for at least one justification window, a baseline justification value based on a summation of the pixel values of historical image data corresponding to the at least one justification window;   calculating, for the at least one justification window, an active view justification value based on a summation of the pixel values of the current image data corresponding to the at least one justification window;   applying a justification factor based on a difference between the baseline view justification value and the active view justification value to pixel values in at least portions of the current image corresponding to the plurality of regions.   
     
     
         5 . The method of  claim 4 , wherein the at least one justification window comprises a plurality of justification windows. 
     
     
         6 . The method of  claim 4 , wherein the justification value is a per-pixel justification value that is further based on the number of pixels in the at least one justification window. 
     
     
         7 . The method of  claim 2 , further comprising the step of:
 transferring the justified image data for any region corresponding to a detected hazard to a classification process for classifying an object in the region corresponding to the detected hazard, the classification process being configured to process only regions of the justified current image corresponding to a potential hazard condition.   
     
     
         8 . The method of  claim 7 , wherein the classification process is optimized to classify human beings, highway vehicles, animals, railcars, locomotives, trees, poles, rock slides, washouts, and misaligned track. 
     
     
         9 . The method of  claim 7 , further comprising:
 in response to the classification process being unable to classify the potential hazard condition within a predetermined time period or with a predetermined certainty, assigning a highest severity index to the hazardous condition.   
     
     
         10 . The method of  claim 2 , further comprising the step of tracking movement of an object corresponding to a potential hazard condition. 
     
     
         11 . The method of  claim 2 , further comprising:
 accessing, in the baseline navigation data, a baseline unobstructed distance corresponding to the position of the train, the baseline unobstructed distance representing a distance to a potential obstruction as measured by reflective technology prior to a current trip of the train; and   accessing, from reflective technology mounted on the train, a current unobstructed distance representing a distance to a potential obstruction as measured by reflective technology;   wherein the step of determining a potential hazard is further based on a comparison of the baseline unobstructed distance and the current unobstructed difference.   
     
     
         12 . The method of  claim 2 , wherein the further comprising the steps of:
 determining that the train is in a multi-track location;   receiving an indication that an other rail vehicle is present on a nearby track; and   reducing the number of the plurality of regions of track in advance of the position of the train for which baseline navigation data is accessed to compensate for a reduction in visibility resulting from the presence of the other rail vehicle on the nearby track.   
     
     
         13 . The method of  claim 2 , wherein the track in advance of the position of the train is divided into a plurality of sections, and plurality of regions includes an impact zone and warning zones on each side of the impact zone for each section of track. 
     
     
         14 . The method of  claim 13 , further comprising the step of issuing a warning when a hazardous condition is detected in a warning zone. 
     
     
         15 . The method of  claim 13 , further comprising the step of issuing a warning when a hazardous condition is detected in an impact zone. 
     
     
         16 . The method of  claim 13 , further comprising the step of issuing an indication that the train's brakes should be activated in response to a detection of a hazardous condition that corresponds to a condition requiring brake activation in an impact zone at a distance which is substantially equal to or less than a distance in which it is possible to stop the train. 
     
     
         17 . The method of  claim 2 , wherein the current image is obtained from a sensor mounted on the train. 
     
     
         18 . The method of  claim 2 , wherein the current image is obtained from a sensor that is not mounted on the train, the sensor being selected from the group consisting of a sensor mounted on a drone and a sensor mounted in a fixed location on a track wayside. 
     
     
         19 . The method of  claim 2 , wherein the sensor is an image collection device configured to image light in the visible spectrum. 
     
     
         20 . A method for developing baseline navigation data comprising:
 determining that a rail vehicle has traveled a fixed distance along a length of track;   in response to the determination, capturing an image from a camera mounted to a rail vehicle and determining an unobstructed distance ahead of the rail vehicle using a reflective technology sensor;   detecting an unobstructed distance in the absence of any hazardous condition using reflective technology at each location corresponding to an image;   defining, for each image, a coordinates for a plurality of regions of track in advance of the position of the rail vehicle;   calculating, for each region in the image, a pixel calculus value;   calculating, for at least one justification window in the image, a pixel calculus value;   identifying at least one pixel for at least one rail in the image;   storing, for each image, a record comprising the pixel calculus value for each of the plurality of regions in the image, the coordinates of each of the regions in the image, the pixel calculus value for the at least one justification window in the image, alignment data comprising coordinates for the at least one pixel of the at least one rail, and the unobstructed distance corresponding to the location of the image.