Autonomous Vehicle Architecture Inspired by the Neu-rocognition of Human Driving


Presented at 4th International Confernce on Vehicle Tehnology and Intelligent Transport Systems, March 2018


The realization of Autonomous vehicles is recognized as a relevant objective for the modern society and con-
stitutes a challenge which in the last decade is concentrating a growing interest, involving both manufacturers
and research institutes. The standard approach to the realization of automated driving agents is based on a
well-known paradigm, consisting of the sense-think-act scheme. Even though this implements an understand-
able and agreeable logic, a driving agent based on such an approach needs to be tested and qualified at a level
of reliability which requires a huge experimental campaign. In this position paper the scope of the problem
of automated driving is widened into the cognitive sciences, where the inspiration is taken to reformulate
the underlying paradigm of the automated agent architecture. In the framework of the EU Horizon 2020
Dreams4Cars Research and Innovation Action project the challenge is to design and train an automated driv-
ing agent which mimics the known human cognitive architecture and as such is able to learn from significant
situations encountered (either simulated or experienced), rather than simply applying a set of fixed rules.


This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731593.