Angewandte Technisch-Kognitive Systeme/en

= Applied Technical Cognitive Systems=

Prof. Dr. J. Marius Zöllner Office: Sonja Göttl Teresa Panhans

 

Description

The research focuses on technologies of applied machine intelligence. Based on fundamental research new systems such as autonomous service robots, autonomous vehicles or assistance systems with cognitive capabilities are to be realized. The use of these so-called technical-cognitive systems takes place primarily in the context of highly automated, efficient and intermodal mobility; connected, automated production and logistics as well as the interactive support of the user in everyday situations.

Perception, situation assessment as well as decision making are the primarily addressed basics of machine intelligence. Methods for machine learning and probabilistic inference are thereby researched and applied for all components. The holistic use of neural methods in the areas of adaptive perception and behavioral decision making is being accounted for in the long term with the newly formed research focus of neurorobotics. Procedures for system evaluation and validation form an additional focus in the context of applied research. Autonomous vehicles like CoCar and CoCar-Zero, mobile robots such as the assistant robot Hollie, the walking robot Lauron or the inspection robot Cairo thereby form valuable integration and evaluation platforms for applied research. They are developed in close cooperation with the FZI and used for joint research and teaching.

News

	2020-09-21:	Best Dissertation Award - IEEE ITS Society
	2018-11-15:	Audi Autonomous Driving Cup 2018: Team AlpaKa wins the title
	2018-11-05:	Best Paper Award - IEEE International Conference on Intelligent Transportation Systems (ITSC)
	2018-06-28:	Best Paper Award - IEEE Intelligent Vehicles Symposium (IV)
	2018-06-28:	Best Paper Award - IEEE Intelligent Vehicles Symposium (IV)

Links

Open Thesis Projects Automated/Autonomous Driving - Prediction and Planning in the Context of Cooperative Driving Machine Learning applied to Automated/Autonomous Driving - Safe Cooperative Multi-Agent Trajectory Planning Machine Learning applied to Automated/Autonomous Driving - Cooperative Multi-Agent Reinforcement Learning with Safety Assurances Machine Learning applied to Automated/Autonomous Driving - Learning Cost Metrics for Cooperative Driving Open Positions Student Assistant | Cooperatively Interacting Automobiles Student Assistant | Systemadministration Research Scientist | Highly Automated Driving Our Partner Institutes Forschungsbereich ISPE

People

• Daniel Bogdoll
• Johannes Buyer
• Mohammd Karam Daaboul
• Jens Doll
• Tobias Fleck
• Daniel Grimm
• Marc Heinrich
• Christian Hubschneider
• Mark Timon Hüneberg
• Tim Joseph
• Florian Kuhnt
• Karl Kurzer
• Nico Lambig
• Sven Ochs
• Stefan Orf
• Svetlana Pavlitskaya
• Nikolai Polley
• Albert Schotschneider
• Philip Schörner
• Philipp Stegmaier
• Lars Töttel
• Abhishek Vivekanandan
• Michael Weber
• Maximilian Zipfl
• Marc Rene Zofka
• J. Marius Zöllner

Courses

• Machine Learning 1 - Fundamental Methods, lecture (takes place in winter semester)
• Machine Learning 2 – Advanced Methods, lecture (takes place in summer semester)
• Programming I: Java, lecture (takes place in winter semester)
• Lab Course Cognitive Automobiles, practical course (takes place in winter semester)
• Lab Course Machine Learning, practical course (takes place in summer semester)

Active Projects

LernSmArt

SPP 1835: Kooperativ interagierende Automobile External Link: http://www.coincar.de/#/

Testfeld Autonomes Fahren External Link: https://taf-bw.de/