  The railway industry is searching for new ways to automate a number of
complex train functions, such as object detection, track discrimination, and
accurate train positioning, which require the artificial perception of the
railway environment through different types of sensors, including cameras,
LiDARs, wheel encoders, and inertial measurement units. A promising approach
for processing such sensory data is the use of deep learning models, which
proved to achieve excellent performance in other application domains, as
robotics and self-driving cars. However, testing new algorithms and solutions
requires the availability of a large amount of labeled data, acquired in
different scenarios and operating conditions, which are difficult to obtain in
a real railway setting due to strict regulations and practical constraints in
accessing the trackside infrastructure and equipping a train with the required
sensors. To address such difficulties, this paper presents a visual simulation
framework able to generate realistic railway scenarios in a virtual environment
and automatically produce inertial data and labeled datasets from emulated
LiDARs and cameras useful for training deep neural networks or testing
innovative algorithms. A set of experimental results are reported to show the
effectiveness of the proposed approach.
