<?xml version="1.0" encoding="utf-8"?><!DOCTYPE article  PUBLIC '-//OASIS//DTD DocBook XML V4.4//EN'  'http://www.docbook.org/xml/4.4/docbookx.dtd'><article><articleinfo><title>AbstractSorinGrigorescu</title><revhistory><revision><revnumber>2</revnumber><date>2017-12-06 16:15:33</date><authorinitials>DanielaZaharie</authorinitials></revision><revision><revnumber>1</revnumber><date>2017-12-06 16:14:57</date><authorinitials>DanielaZaharie</authorinitials></revision></revhistory></articleinfo><para>Title: <emphasis role="strong">Self-learning Software for Predictive Systems in Autonomous Driving</emphasis> </para><para><emphasis role="strong">Abstract:</emphasis> </para><para>The next steps towards human-like AI autonomous driven cars are paved by the development of self-adapting software systems that can reason beyond traditional deep learning perception methods. In this talk, an insight into current AI activities undergoing within Elektrobit will be given. This includes <emphasis>Deep Grid Net</emphasis>, a deep neural network designed for inferring context awareness from grid data, Elektrobit’s approach to learning how to drive in a simulator using Deep Reinforcement Learning, as well as our <emphasis>Generative One-shot Learning</emphasis> framework built to learn representations of data patterns from single object instances. </para></article>