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Robust and Reliable Environment Sensing and Situation Prediction for Advanced Driver Assistance Systems and Automated Driving


Robust and Reliable Environment Sensing and Situation Prediction for Advanced Driver Assistance Systems and Automated Driving

Link to CORDIS:
Background & policy context: 

RobustSENSE vision is an autonomous vehicle that makes driving safer and more comfortable for vehicle passengers and other road users in all existing driving conditions.

The advancement of automated driving and ADAS is closely linked to the capability of environmental perception. Perceiving and understanding the environment and ultimately predicting the movement of objects in the vehicle’s surroundings are keyfactors to mastering automated driving. The systems on the market are reliable in good environmental conditions. However, in adverse weather and lightning conditions the picture changes dramatically and in these situations with increased risk of accident, human control is still needed.

Without excellent and reliable technology to enable automated driving in all environmental conditions,fully autonomous vehicles will not become reality on the roads. Therefore, the capability of the sensing systems and subsequent processing units to cope with these environmental challenges is on the top of the respective research agenda.


RobustSENSE develops a robust and reliable sensor platform for automated driving. This platform addresses the problem that today’s driver assistance systems may stop working in harsh environmental conditions like snow, rain or sun-flare. An improved platform is needed to achieve the necessary reliability of automated driving functions for safe operation in all driving conditions.

RobustSENSE aims at enhancing the robustness of all sensing methods and algorithms required for advanced driver assistance systems and automated driving. The project will contribute to improved road safety with a more reliable, secure and trustable sensor platform:

  • Reliable for safe operation under all driving conditions.
  • Improving operability by self-diagnosis, adaptation and robustness.
  • Trustable on every level of assistance and automation.
Institution Type:
Type of funding:
Programme Other: 
ECSEL Joint Undertaking
  • Daimler AG
  • EICT GmbH - European Center for Information and Communication Technologies
  • AVL List GmbH
  • AVL Deutschland GmbH
  • Robert Bosch GmbH
  • Centro Ricerche Fiat
  • Centro Tecnológico de Automoción de Galicia
  • Fico Mirrors S.A.
  • FOKUS - Fraunhofer Institute for Open Communication Systems
  • FZI Forschungszentrum Informatik
  • Modulight, Inc.
  • Oplatek Group Oy
  • Universität Ulm
  • Teknologian Tutkimuskeskus VTT Oy
EUREF-Campus 13
Contact country:
+49 30 3670235 127
Development phase: