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


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.


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


Probabilistic Modelling

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) ; 2017

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

Sebastian Feuerstack and Bertram Wortelen; Kognitive Systeme; 2017

A Tool-based Process for Generating Attention Distribution Predictions

Feuerstack, S. and Wortelen, B.; Proceedings of the 19th European Conference on Eye Movements ; 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

Tutorial: How does your HMI Design affect the visual attention of the driver?

Sebastian Feuerstack and Bertram Wortelen; Adjunct Proceedings of the 9th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’17); 2017

Predicting Visual Attention is not an easy Task - even for Experts!

Sebastian Feuerstack and Bertram Wortelen; 60th Conference of Experimental Psychologists; 0March / 2018

Flyer: HEE and Konect, Optimizing HMIs for Efficient Monitoring

Sebastian Feuerstack and Bertram Wortelen; 01 / 2018

Dynamic Bayesian networks for driver-intention recognition based on the traffic situation

Eilers, Mark and Fathiazar, Elham and Suck, Stefan and Twumasi, Daniel; Cooperative Intelligent Transport Systems: Towards High-Level Automated Driving; 009 / 2019

Broadbit Energy Technologies s.r.o
Continental Automotive France SAS
CRF - Centro Ricerche Fiat S.C.p.A.
Deutsches Zentrum für Luft- und Raumfahrt e.V.
HuMaTects GmbH
Universität Ulm


Start: 31.08.2016
End: 30.08.2019

Website of project

Source of funding

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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