Variational Autoencoder Inspired by Brain's Convergence-Divergence Zones for Autonomous Driving Application

In the last decades, the research in autonomous vehicles has greatly improved thanks to the success of artificial neural models. Yet, self-driving cars are far from reaching human performances. It is our opinion that would be wise to reflect on why the human brain is so effective in learning tasks as complex as the one of driving, and to try to take inspiration for designing new artificial driving agents. For this aim, we consider two relevant and related neurocognitive theories: the Convergence-divergence Zones (CDZs) mechanism of mental simulation, and the predicting brain theory. Then, we propose an implementation of a semi-supervised variational autoencoder for visual perception, with an architecture that best approximates those two neurocognitive theories.