1) Exit Strategies from COVID-19 Lockdown: Based on the paper "Modeling Exit Strategies from COVID-19 lockdown with a Focus on Antibody Test", the already existing system dynamics model can be extended or an own agent-based model can be developed. The simulations could focus on the effects of the "Contact-Tracing Apps", different age groups, or the influence of vaccinations. Further considerations and ideas are welcome. The corresponding paper is available on the chair website. The paper presents two epidemiological models that have been developed in order to study the disease dynamics of the COVID-19 pandemic and exit strategies from the lockdown which has been imposed on many countries world-wide. A strategy is needed such that both the health system is not overloaded letting people die in an uncontrolled way and also such that the majority of people can get back their social contacts as soon as possible. We investigate the potential effects of a combination of measures such as continuation of hygienic constraints after leaving lockdown, isolation of infectious persons, repeated and adaptive short-term contact reductions and also large-scale use of antibody tests in order to know who can be assumed to be immune and participate at public life without constraints. We apply two commonly used modeling approaches: extended SEIR models formulated both as System Dynamics and Agent-Based Simulation, in order to get insight into the disease dynamics of a complete country like Germany and also into more detailed behavior of smaller regions. We confirm the findings of other models that without intervention the consequences of the pandemic can be catastrophic and we extend such findings with effective strategies to overcome the challenge. Based on the modeling assumptions it can be expected that repeated short-term contact reductions will be necessary in the next years to avoid overload of the health system and that on the other side herd immunity can be achieved and antibody tests are an effective way to mitigate the contact reductions for many.

2) Railway Network Simulation: With the developing technologies and methods in the field of real-time communication and the constantly increasing amount of data to be transmitted, the railway industry has jumped on the bandwagon of modernizing its processes. The aim is to merge the separate networks for train control and non-critical information, e.g. for passenger information, and also to be compatible with other train manufacturers. In the field of real-time communication, Time-Sensitive Networking (TSN) has emerged as a possible solution to overcome the above-mentioned challenges. It provides procedures and mechanisms for Ethernet technology, enriching it with aspects of determinism and reliability. TSN enables the sharing of real-time and best-effort traffic on a single line. As there are only a few TSN-enabled devices on the market so far, the validation of TSN technology is limited. Simulation offers a good alternative in this situation. The tool OMNeT++ is a modular, C++ based framework for network simulation. A suitable TSN library already exists for this purpose, which covers the most important mechanisms for real-time communication. Within the project, a next-generation train network with TSN mechanisms will be built and the necessary cyclic TSN messages, like brake signals, and acyclic or stochastic messages like passenger information will be defined. Finally, the simulation shall be evaluated. Thereby, the focus will be on maintaining real-time capability and the use of bandwidth. All mechanisms and information are already given for the setup of the simulation. An introduction to TSN will be given.

3) High Level Sensor Models: The development and testing of automated driving functions in the real world is costly and time-consuming. For this reason, software for automated driving is at first developed and tested in a virtual environment. The provision of the virtual environment requires the coupling of several simulation tools and models. An essential feature of the simulation setup are the sensor models. A distinction is made between low-level models and high-level models. Low-level models replicate physical effects, e.g. through ray tracing. They are highly precise, but they require a lot of computing power. High-level models filter an object list based on geometric constraints. They are less accurate, but faster than the low-level sensor models. The aim of the project is the design and implementation of a high level sensor model. The sensor model receives as input a list of all objects available in the virtual world of the submicroscopic traffic simulator "Carla". As output, the sensor model provides all objects in the visibility range of the ego-vehicle. The programming is carried out in Python, for which "Carla" offers an API.

Title:

Exercises to: Simulation and Modeling II

Credits: 5

Prerequisites

Prior participation in the course "Simulation and Modeling 1" is strongly recommended.

Contents

The participants conduct a simulation project over the whole semester in groups of 3 to 4. The participants may submit project proposals or work on one of the following projects:

1) Exit Strategies from COVID-19 Lockdown: Based on the paper "Modeling Exit Strategies from COVID-19 lockdown with a Focus on Antibody Test", the already existing system dynamics model can be extended or an own agent-based model can be developed. The simulations could focus on the effects of the "Contact-Tracing Apps", different age groups, or the influence of vaccinations. Further considerations and ideas are welcome. The corresponding paper is available on the chair website. The paper presents two epidemiological models that have been developed in order to study the disease dynamics of the COVID-19 pandemic and exit strategies from the lockdown which has been imposed on many countries world-wide. A strategy is needed such that both the health system is not overloaded letting people die in an uncontrolled way and also such that the majority of people can get back their social contacts as soon as possible. We investigate the potential effects of a combination of measures such as continuation of hygienic constraints after leaving lockdown, isolation of infectious persons, repeated and adaptive short-term contact reductions and also large-scale use of antibody tests in order to know who can be assumed to be immune and participate at public life without constraints. We apply two commonly used modeling approaches: extended SEIR models formulated both as System Dynamics and Agent-Based Simulation, in order to get insight into the disease dynamics of a complete country like Germany and also into more detailed behavior of smaller regions. We confirm the findings of other models that without intervention the consequences of the pandemic can be catastrophic and we extend such findings with effective strategies to overcome the challenge. Based on the modeling assumptions it can be expected that repeated short-term contact reductions will be necessary in the next years to avoid overload of the health system and that on the other side herd immunity can be achieved and antibody tests are an effective way to mitigate the contact reductions for many.

2) Railway Network Simulation: With the developing technologies and methods in the field of real-time communication and the constantly increasing amount of data to be transmitted, the railway industry has jumped on the bandwagon of modernizing its processes. The aim is to merge the separate networks for train control and non-critical information, e.g. for passenger information, and also to be compatible with other train manufacturers. In the field of real-time communication, Time-Sensitive Networking (TSN) has emerged as a possible solution to overcome the above-mentioned challenges. It provides procedures and mechanisms for Ethernet technology, enriching it with aspects of determinism and reliability. TSN enables the sharing of real-time and best-effort traffic on a single line. As there are only a few TSN-enabled devices on the market so far, the validation of TSN technology is limited. Simulation offers a good alternative in this situation. The tool OMNeT++ is a modular, C++ based framework for network simulation. A suitable TSN library already exists for this purpose, which covers the most important mechanisms for real-time communication. Within the project, a next-generation train network with TSN mechanisms will be built and the necessary cyclic TSN messages, like brake signals, and acyclic or stochastic messages like passenger information will be defined. Finally, the simulation shall be evaluated. Thereby, the focus will be on maintaining real-time capability and the use of bandwidth. All mechanisms and information are already given for the setup of the simulation. An introduction to TSN will be given.

3) High Level Sensor Models: The development and testing of automated driving functions in the real world is costly and time-consuming. For this reason, software for automated driving is at first developed and tested in a virtual environment. The provision of the virtual environment requires the coupling of several simulation tools and models. An essential feature of the simulation setup are the sensor models. A distinction is made between low-level models and high-level models. Low-level models replicate physical effects, e.g. through ray tracing. They are highly precise, but they require a lot of computing power. High-level models filter an object list based on geometric constraints. They are less accurate, but faster than the low-level sensor models. The aim of the project is the design and implementation of a high level sensor model. The sensor model receives as input a list of all objects available in the virtual world of the submicroscopic traffic simulator "Carla". As output, the sensor model provides all objects in the visibility range of the ego-vehicle. The programming is carried out in Python, for which "Carla" offers an API.