  One essential feature of an autonomous train is minimizing collision risks
with third-party objects. To estimate the risk, the control system must
identify topological information of all the rail routes ahead on which the
train can possibly move, especially within merging or diverging rails. This
way, the train can figure out the status of potential obstacles with respect to
its route and hence, make a timely decision. Numerous studies have successfully
extracted all rail tracks as a whole within forward-looking images without
considering element instances. Still, some image-based methods have employed
hard-coded prior knowledge of railway geometry on 3D data to associate
left-right rails and generate rail route instances. However, we propose a rail
path extraction pipeline in which left-right rail pixels of each rail route
instance are extracted and associated through a fully convolutional
encoder-decoder architecture called TPE-Net. Two different regression branches
for TPE-Net are proposed to regress the locations of center points of each rail
route, along with their corresponding left-right pixels. Extracted rail pixels
are then spatially clustered to generate topological information of all the
possible train routes (ego-paths), discarding non-ego-path ones. Experimental
results on a challenging, publicly released benchmark show true-positive-pixel
level average precision and recall of 0.9207 and 0.8721, respectively, at about
12 frames per second. Even though our evaluation results are not higher than
the SOTA, the proposed regression pipeline performs remarkably in extracting
the correspondences by looking once at the image. It generates strong rail
route hypotheses without reliance on camera parameters, 3D data, and
geometrical constraints.
