Key Info

Basic Information

Prof. Dr. Klaus Steffen Leonhardt
Faculty / Institution:
Electrical Engineering and Information Technology
Societal Challenges
Project duration:
01.09.2016 to 29.02.2020
EU contribution:
8.998.950 euros
  EU flag This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688900.  


Adaptive ADAS to support incapacitated drivers Mitigate Effectively risks through tailor made HMI under automation


ADAS&ME (“Adaptive ADAS to support incapacitated drivers &Mitigate Effectively risks through tailor made HMI under automation”) will develop adapted Advanced Driver Assistance Systems, that incorporate driver/rider state, situational/environmental context, and adaptive interaction to automatically transfer control between vehicle and driver/rider and thus ensure safer and more efficient road usage. To achieve this, a holistic approach will be taken which considers automated driving along with information on driver/rider state. The work is based around 7 provisionally identified Use Cases for cars, trucks, buses and motorcycles, aiming to cover a large proportion of driving on European roads. Experimental research will be carried out on algorithms for driver state monitoring as well as on HMI and automation transitions. It will develop robust detection/prediction algorithms for driver/rider state monitoring towards different driver states, such as fatigue, sleepiness, stress, inattention and impairing emotions, employing existing and novel sensing technologies, taking into account traffic and weather conditions via V2X and personalizing them to individual driver’s physiology and driving behaviour. In addition, the core development includes multimodal and adaptive warning and intervention strategies based on current driver state and severity of scenarios. The final outcome is the successful fusion of the developed elements into an integrated driver/rider state monitoring system, able to both be utilized in and be supported by vehicle automation of Levels 1 to 4. The system will be validated with a wide pool of drivers/riders under simulated and real road conditions and under different driver/rider states; with the use of 2 cars (1 conventional, 1 electric), 1 truck, 2 PTWs and 1 bus demonstrators. This challenging task has been undertaken by a multidisciplinary Consortium of 30 Partners, including an OEM per vehicle type and 7 Tier 1 suppliers.


  • Statens Vag-Och Transportforskningsinstitut, Sweden (Coordinator)
  • Ethniko Kentro Erevnas Kai Technologikis Anaptyxis, Greece
  • Ducati Motor Holding S.p.A., Italy
  • Ford-Werke GmbH, Germany
  • Scania CV AB, Sweden
  • École polytechnique fédérale de Lausanne, Switzerland
  • Autoliv Development AB, Sweden
  • Continental Automotive GmbH, Germany
  • Dainese S.p.a., Italy
  • Denso Automotive Deutschland GmbH, Germany
  • TOMTOM International BV, Netherlands (Participation ended)
  • VALEO Comfort and Driving Assistance Systems, France
  • Deutsches Zentrum für Luft- und Raumfahrt e.V., Germany
  • IDIADA Automotive Technology SA, Spain
  • Institut français des sciences et technologies des transports, de l'aménagement et des réseaux, France
  • Stockholms Universitet, Sweden
  • Università degli Studi di Roma La Sapienza, Italy
  • Fraunhofer Gesellschaft zur Förderung der angewandten Forschung e.V., Germany
  • National University of Ireland Galway, Ireland
  • Rheinisch-Westfälische Technische Hochschule Aachen, Germany
  • Smart Eye Aktiebolag, Sweden
  • Foundation for Research and Technology Hellas, Greece
  • Technische Universität Chemnitz, Germany
  • Panepistimio Patron, Greece
  • Uppsala Universitet, Sweden
  • Automobil Club Assistencia SA, Spain
  • Humanist, France
  • Osborne Clarke, Belgium
  • Otto-von-Guericke-Universität Magdeburg, Germany
  • Fondation Partenarial MOV'EOTEC, France
  • Tomtom Global Content B.V., Netherlands