AutoMate Automation as accepted and Trusted TeamMate to enhance traffic safety and efficiency

Motivation

The success of future more complex and more automated vehicles will depend on how well they interact, communicate, and cooperate with humans both inside and outside the vehicle. Driver and automation have to be considered as team members who share the driving task and who both are responsible for the safety of driving. The object of design is not the automation system but the overall driver-automation team.

Ziele

AutoMate will create a highly reliable automated driving system that users can understand, accept, trust and eventually will use regularly.

Technologien

Probabilistic Modelling

Personen

Projektleitung Intern

Wissenschaftliche Leitung

Publikationen
A Model-driven Tool for getting Insights into Car Drivers’ Monitoring Behavior

Sebastian Feuerstack and Bertram Wortelen; Proceedings of the IEEE Intelligent Vehicles Symposium (IV'17) (in press); 2017

A Tool-based Process for Generating Attention Distribution Predictions

Feuerstack, S. and Wortelen, B.; Proceedings of the 19th European Conference on Eye Movements (in press); 2017

Comparing the Input Validity of Model-based Visual Attention Predictions based on presenting Exemplary Situations either as Videos or Static Images

Bertram Wortelen and Sebastian Feuerstack; ICCM - 15th International Conference on Cognitive Modelling; 2017

The Human Efficiency Evaluator - A tool to predict and analyse monitoring behaviour;

Sebastian Feuerstack and Bertram Wortelen; Kognitive Systeme; 2017

Partner
Broadbit Energy Technologies s.r.o
www.broadbit.com
Continental Automotive France SAS
www.continental-pneumatici.it
Centro Ricerche Fiat S.C.p.A.
www.crf.it
Deutsches Zentrum für Luft- und Raumfahrt e.V.
www.dlr.de
HuMaTects GmbH
www.humatects.com
Universität Ulm
www.uni-ulm.de
VEDECOM
www.vedecom.fr
AutoMate

Ansprechpartner

Laufzeit

Start: 01.09.2016
Ende: 31.08.2019

Website des Projekts

Fördermittelgeber

EU H2020

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no.690705.

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