Angewandte Technisch-Kognitive Systeme/en

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.

CoCar NextGen at IEEE ITSC 2023 At the 2023 International Conference on Intelligent Transportation Systems we unveiled our newest autonomous and connected research vehicle: CoCar NextGen. Based on an Audi A6 platform, the vehicle is equipped with the latest sensor and computing technology to support our autonomous driving software. Its purpose is to facilitate research in connected and autonomous driving through real-world measurement and test drives. The vehicle features nine cameras, twelve LiDAR sensors, three radar sensors and an enhanced GPS system. A high-performance server powers our autonomous driving software, complemented by multiple integrated GPUs to enhance our AI capabilities. For communication with other vehicles and intelligent infrastructure, such as our Testfeld Autonomes Fahren in Karlsruhe, CoCar NextGen incorporates modern Vehicle-To-Everything (V2X) modules. Additionally, we presented eight research papers at ITSC 2023, underscoring our commitment to advancing autonomous and connected driving systems. If you too are interested in contributing to our research endeavors involving CoCar NextGen or our portfolio of autonomous connected vehicles and intelligent infrastructure, we invite you to reach out regarding Hiwi, Bachelor and Master Theses

Open Theses and HiWi-Jobs Research Area Research Topics Perception Traffic Light Detection with Deep Neural Networks for Autonomous Driving Aerial Image Segmentation with Deep Neural Networks for Autonomous Driving Anomaly Detection for Autonomous Driving Generative World Models for Future Predictions Deep Learning based 3D Environment Perception for Autonomous Vehicles Sensor-setup agnostic Machine Learning Perception for Autonomous Vehicles Robust, Interpretable and Energy-Efficient Deep Learning for Camera-based Perception Sensorfusion for Connected Autonomous Driving Intelligent Roadside Infrastructure for Connected Autonomous Driving Recognition of Sensor Data Discrepancies in Autonomous Vehicles and Smart Infrastructure Perception with Intelligent Traffic Infrastructure Prediction Predicting the Behavior of Traffic Participants with Deep Neural Networks Behavior and Motion Prediction of Traffic Participants for Safe Trajectory Planning Probabilistic Decision Making and Scene Interpretation A priori VRU Behavior Prediction using Traffic Infrastructure for Autonomous Driving Trajectory forecasting under road topology and vehicle constraints UX Automated Capture of User Experience in Autonomous Vehicles Maps Automated Generation and Maintenance of Probabilistic HD-Maps Aerial Image Segmentation with Deep Neural Networks for Autonomous Driving Semantic LiDAR-Localization and Validation through GPS and Odometrie Planning Cooperative Trajectory Planning under Uncertainties Probabilistic Decision Making and Scene Interpretation Safety and Security Misbehavior Detection and Optimization of Driving Components for Robustness Improvement Evaluating Metrics for Performance Assessment in Autonomous Driving Adversarial Attacks on Deep Learning Models Condition Monitoring for Robust and Safe Autonomous Systems Vehicle-to-Everything (V2X/Car2X) Vehicle-to-everything (V2X) for Distributed Simulations on Proving Grounds and Test Areas for Autonomous Driving Interactive Machine Learning for Remote Assisted Autonomous Vehicles Remote Operation in Autonomous Driving Simulation Distributed Virtual Reality (VR) and Simulation Frameworks for Validation and Verification of Autonomous Vehicles Simulation of Autonomous Driving and Behaviour Modelling of Vulnerable Road Users Rare and challenging scenarios in simulation Reinforcement Learning Distributional and Model-Based Reinforcement Learning Efficient Unsupervised Reinforcement Learning End-to-End autonomous driving using Reinforcement Learning Other Topics in Autonomous Driving Postprocessing of Trajectory Tracking Vehicle Hardware and Sensor Setups for Autonomous Vehicles

Our Partner Institutes

Intelligent Systems and Production Engineering

People

• Ahmed Abouelazm
• Daniel Bogdoll
• Johannes Buyer
• Mohammd Karam Daaboul
• Jens Doll
• Marcus Fechner
• Tobias Fleck
• Martin Gontscharow
• Helen Gremmelmaier
• Daniel Grimm
• Dominik Heid
• Marc Heinrich
• Christian Hubschneider
• Tim Joseph
• Florian Kuhnt
• Karl Kurzer
• Nico Lambing
• Albert Lee
• Ferdinand Mütsch
• Sven Ochs
• Stefan Orf
• Stefan Orf/en
• Svetlana Pavlitskaya
• Nikolai Polley
• Rupert Polley
• Helen Schneider
• Albert Schotschneider
• Philip Schörner
• Philipp Stegmaier
• Marc Uecker
• Abhishek Vivekanandan
• Michael Weber
• Melih Yazgan
• Maximilian Zipfl
• Marc Zofka
• J. Marius Zöllner

Courses

• Advanced Programming - Java Network Programming, lecture (takes place in summer semester)
• 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)
• Cognitive automobiles and robots, seminar (takes place in )
• Lab Course Cognitive Automobiles, practical course (takes place in winter semester)
• Lab Course Machine Learning, practical course (takes place in summer semester)

Active Projects

DigiT4TAF

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

SofDCar External Link: https://www.sofdcar.de

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