Virtual conference systems provide an alternative to physical meetings that have significantly grown in importance over the last years. However, larger events require the audience to be muted to avoid an accumulation of background noise and distorted audio. While this is sufficient for unidirectional meetings, many types of meetings strongly rely on the feedback of their audience, such as in performing arts.
In this project, we want to extend Zoom sessions with a simple Chatbot that collects the audience participation of each user using a straightforward button interface. Then, the system renders the overall audience feedback based on the feedback state collected from each user. The project combines signal and audio processing with the chance to gain practical experience with app development and SDKs.

References

• Create a Chatbot App
• Build a Chatbot

• Good knowledge in Nodejs/JavaScript
• Experience with Git
• Experience with Zoom SDK would be a plus

This work can be done in German or English in a team of 2-4 members.
Self-driving cars need to be safe in the interaction with other road users such as motorists, cyclists, and pedestrians. But how can car manufacturers ensure that their self-driving cars are safe with us humans? The only realistic and economic way to test this is to use simulation.
cogniBIT is a Munich-based Startup founded by Alumni of TUM and LMU and provides realistic models of all kind of road users. These models are based on state-of-the art neurocognitive and sensorimotor research and reproduce human perception, cognition, and action with all its limitations.
In this project the objective is to extend the open-source simulator CARLA (www.carla.org) such that German Autobahn-like scenarios can be simulated.

Tasks:
•    Design an Autobahn scenario using the road description format OpenDRIVE.
•    Adapt the CARLA OpenDRIVE standalone mode (requires C++ knowledge).
•    Design an environment for the scenario using the Unreal Engine 4 Editor.
•    Perform a simulation-based experiment using the German Autobahn scenario and the cogniBIT driver model.

•    C++ knowledge
•    experience with Python is helpful
•    experience with the UE4 editor is helpful
•    interest in autonomous driving and cognitive models

This work can be done in German or English in a team of 2-4 members.
Self-driving cars need to be safe in the interaction with other road users such as motorists, cyclists, and pedestrians. But how can car manufacturers ensure that their self-driving cars are safe with us humans? The only realistic and economic way to test this is to use simulation.
cogniBIT is a Munich-based Startup founded by Alumni of TUM and LMU and provides realistic models of all kind of road users. These models are based on state-of-the art neurocognitive and sensorimotor research and reproduce human perception, cognition, and action with all its limitations.
In this project the objective is to extend the open-source simulator CARLA (www.carla.org) such that German Autobahn-like scenarios can be simulated.

Tasks:
•    Design an Autobahn scenario using the road description format OpenDRIVE.
•    Adapt the CARLA OpenDRIVE standalone mode (requires C++ knowledge).
•    Design an environment for the scenario using the Unreal Engine 4 Editor.
•    Perform a simulation-based experiment using the German Autobahn scenario and the cogniBIT driver model.

•    C++ knowledge
•    experience with Python is helpful
•    experience with the UE4 editor is helpful
•    interest in autonomous driving and cognitive models

This work can be done in German or English

Existing teledriving setups use multiple cameras to cover the vehicle's surrounding environment in order to provide the operator with sufficient information of the current traffic situation. However, the importance of individual camera views varies for different driving tasks. Modeling the importance of individual camera view according to the current traffic situation can be used in several applications for teledriving.

The goal of this project is the creation and conduction of a user study for measuring the influence of traffic-aware view adaptation. The goal of the user study is to evaluate the performance a our traffic-aware view adaptation compared to a simple uniform bit budget distribution among all camera views.

Tasks

• Introduction to the existing TELECARLA driving setup [1]
• Design a driving user study with the CARLA scenario runner
• Evaluate the results in terms of driving performance, lane invasions, etc.

References

[1] TELECARLA: An Open Source Extension of the CARLA Simulator for Teleoperated Driving Research Using Off-the-Shelf Components, Markus Hofbauer, Christopher B. Kuhn, Goran Petrovic, Eckehard Steinbach; IV 2020

• Experience with ROS (C++ and Python)

This work can be done in German or English

The transmission of control to the human driver in autonomous driving requires the observation of the human driver. The vehicle has to guarantee that the human driver is aware of the current driving situation. One input source for observing the human driver is based on the driver's gaze.

The objective of this project is to compare two existing approaches for driver observation [1,2]. While [1] measures the driver situation awareness (SA), [2] anticipates the drivers decision. As part of a user study [2] published a gaze dataset. An interesting cross validation would be the comparison of the
SA score generated by [1] and the predicted decision correctness of [2].

Tasks

• Generate ROI predictions [3] from the dataset of [2]
• Estimate the driver SA with the model of [1]
• Compare [1] and [2]
• (Optional) Extend driving experiments

References

[1] Markus Hofbauer, Christopher Kuhn, Lukas Puettner, Goran Petrovic, and Eckehard Steinbach. Measuring driver situation awareness using region-of-interest prediction and eye tracking. In 22nd IEEE International Symposium on Multimedia (ISM), Naples, Italy, Dec 2020.
[2] Pierluigi Vito Amadori, Tobias Fischer, Ruohan Wang, and Yiannis Demiris. Decision Anticipation for Driving Assistance Systems. June 2020.
[3] Markus Hofbauer, Christopher Kuhn, Jiaming Meng, Goran Petrovic, and Eckehard Steinbach. Multi-view region of interest prediction for autonomous driving using semisupervised labeling. In IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, Sep 2020.

• Experience with ROS and Python
• Basic knowledge of Linux

We are looking for a teaching assistant student of our new Software Engineering Lab. In this course we explain basic principles of software engineering such as unit testing, test driven development and how to collaborate in teams [1].

You will act as a teaching assistant to supervise students during the lab session working on their practical homeworks. The tasks of the homeworks are generally C++ coding exercises where the students contribute to a common codebase. This means you should have a good experience in C++, unit testing, and git as this will be an essential part of the homeworks.

References

[1] Winters, Titus, Tom Manshreck, and Hyrum Wright, eds. Software Engineering at Google: Lessons Learned from Programming Over Time. O'Reilly Media, Incorporated, 2020

• Very good knowledge in C++
• Experience with unit testing
• Good understanding of git and collaborative software development

We are looking for a teaching assistant student of our new Software Engineering Lab. In this course we explain basic principles of software engineering such as unit testing, test driven development and how to collaborate in teams [1].

You will act as a teaching assistant to supervise students during the lab session working on their practical homeworks. The tasks of the homeworks are generally C++ coding exercises where the students contribute to a common codebase. This means you should have a good experience in C++, unit testing, and git as this will be an essential part of the homeworks.

References

[1] Winters, Titus, Tom Manshreck, and Hyrum Wright, eds. Software Engineering at Google: Lessons Learned from Programming Over Time. O'Reilly Media, Incorporated, 2020

• Very good knowledge in C++
• Experience with unit testing
• Good understanding of git and collaborative software development