Tag Archives: Team Autonomous Driving

– REACT

The overall goal of REACT is a systematic, safe and validatable approach to developing, training and use of digital reality with the goal to ensure  safe and reliable acting autonomous systems – especially in critical situations. In order to reach this goal, we use methods and concepts of machine learning – especially
Deep Learning and (Deep) Reinforcement Learning (RL) – to learn lower-dimensional submodels of the real world. From these submodels we compile (semi) automatically complex, high-dimensional models in order to identify and simulate the entire range of critical situations. By means of  digital reality, we virtually synthesize  the otherwise missing sensor data of critical situations and train autonomous systems so that they are able to handle critical situations safe and confident.  The aim of the project is to enhance the capabilities of autonomous systems. Therefore we continuously and  systematically validate and  align  synthetic data with reality and adapt the models where necessary.

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