  Advanced sensors are a key to enable self-driving cars technology. Laser
scanner sensors (LiDAR, Light Detection And Ranging) became a fundamental
choice due to its long-range and robustness to low light driving conditions.
The problem of designing a control software for self-driving cars is a complex
task to explicitly formulate in rule-based systems, thus recent approaches rely
on machine learning that can learn those rules from data. The major problem
with such approaches is that the amount of training data required for
generalizing a machine learning model is big, and on the other hand LiDAR data
annotation is very costly compared to other car sensors. An accurate LiDAR
sensor model can cope with such problem. Moreover, its value goes beyond this
because existing LiDAR development, validation, and evaluation platforms and
processes are very costly, and virtual testing and development environments are
still immature in terms of physical properties representation. In this work we
propose a novel Deep Learning-based LiDAR sensor model. This method models the
sensor echos, using a Deep Neural Network to model echo pulse widths learned
from real data using Polar Grid Maps (PGM). We benchmark our model performance
against comprehensive real sensor data and very promising results are achieved
that sets a baseline for future works.
