Project

REACT

Autonomous Driving: Modeling, learning and simulation environment for pedestrian behavior in critical traffic situations

Autonomous Driving: Modeling, learning and simulation environment for pedestrian behavior in critical traffic situations

  • Duration:

REACT is considered to be a strategically important project for the ASR department; the content is also a core topic in the Autonomous Driving Competence Center (CCAD). The overall goal of REACT is a systematic, safe and validatable approach to the development, training and use of digital reality to achieve safe and reliable action by autonomous systems, especially in critical situations. For this purpose, methods and concepts of machine learning - in particular deep learning and (deep) reinforcement learning (RL) - are used to learn low-dimensional submodels of the real world. In this way, a suitably wide range of existing critical situations should be recorded and identified so that they can be simulated in virtual space. Using this digital reality, the otherwise missing sensor data for critical situations can then be virtually synthesized and used to train the autonomous systems and vehicles for the safe and reliable handling of critical situations. The ultimate goal of the project is to systematically validate and constantly improve the capabilities of autonomous systems through continuous alignment with reality and the necessary adaptation of models.

Publications about the project

Arsène Pérard-Gayot, Roland Leißa, Sebastian Hack, Puya Amiri, Richard Membarth, Philipp Slusallek

In: Proceedings of the 2021 International Conference on Field Programmable Technology (ICFPT). International Conference on Field Programmable Technology (FPT-2021) December 6-10 Auckland New Zealand Pages 1-9 IEEE 12/2021.

To the publication
Matthias Klusch, Patrick Gebhard, Tanja Schneeberger

In: Proceedings of 36th ACM Symposium on Applied Computing. ACM Symposium On Applied Computing (SAC-2021) ACM 2021.

To the publication
Yoshiyuki Kobayashi, Lorena Hell, Janis Sprenger, Matthias Klusch, Christian Müller

In: Proceedings of the 32nd IEEE International Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium (IV-2021) July 11-17 Nagoya/Virtual Japan IEEE 2021.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz