Research Projects

(Own Funds)

Abstract:

The possibilities and challenges of vehicle-to-everything communication (V2X communication) have been being researched for several years already. A popular means allowing for sufficient flexibility in the investigations whilst maintaining a relatively high level of detail is the simulation of such networks, which must take both the traffic as well as communication aspects into account. The simulation framework Veins developed at the chair has already proven to be a successful tool.
A limitation of current V2X simulation frameworks is the assumption of a quasi-two-dimensional environment. The various influences of terrain shape, other road participants or communication across multiple road levels usually remain unconsidered. However, due to the mentioned aspects, many real-world traffic scenarios and thus vehicular networks exhibit a three-dimensional character, which is why it must be assumed that they can be analyzed only limitedly with current simulators.
In this project, we seek to investigate whether the above-mentioned assumption holds true. For this purpose, conventional packet-based V2X simulation has to be extended accordingly in order to be able to simulate such scenarios at large scale. This also requires the implementation of new channel models that can realistically depict the three-dimensional character of complex scenarios with limited computational effort. To ensure correct results the new simulation models should be validated with the help of appropriate field tests. Furthermore, the computational effort of complex simulation scenarios is to be reduced by means of suitable techniques and possibly AI methods.

(Own Funds)

Abstract:

Simulation is a decent method to study, evaluate, and validate upcoming technologies and algorithms. In order to generate realistic results, it is necessary to overcome different challenges. One of these challenges is the computational feasibility of holistic simulation scenarios, especially when it comes to large-scale setups. These scenarios may model a whole city or even an entire country. Besides performance problems, adequate modeling of real world scenarios often requires the combination of multiple simulation tools from different domains. This combination often requires the connection of different modeling paradigms. Other challenges tasks are the time synchronization of the different simulation tools and the data exchange between them.
To solve these problems, a hybrid co-simulation framework is developed in this project. The Framework uses an implementation of High Level Architecture (HLA, IEEE1516) as a middleware and enables the dynamic composition of a simulation setup that matches current requirements. The composition takes place in two dimensions. In a vertical dimension, multi-level support empowers the simulation at different levels of detail, corresponding to the demands regarding performance, available data, or posed questions. In a horizontal dimension, the coupling of tools from different domains is enabled. The focus on extendability makes it possible to add any needed tools at a later point in time to the framework.

(Non-FAU Project)

Abstract:

Die Funktionssicherheit von Fahrerassistenzsystemen sowie automatisierter und vernetzter Funktionen ist vom Automobilhersteller in jeder denkbaren Verkehrssituation sicherzustellen.  Im Entwicklungs- und Absicherungsprozess ist dazu eine erhebliche Zahl  von Verkehrssituationen, sog. Szenarien, abzuprüfen.  Dieser umfangreiche Prüfumfang lässt sich in Zukunft nur noch durch den massiven Einsatz von Computersimulation sinnvoll bewältigen. Um in diesen Simulationen eine entsprechende Validität und Praxisrelevanz zu erzeugen, müssen Modelle des eigenen Fahrzeugs, der Strecken und –Umgebung sowie des umgebenden Verkehrs adäquat modelliert werden.

Im Rahmen dieser Arbeit soll eine Methodik zur Absicherung von Systemen und Funktionen des automatisierten und vernetzten Fahrens mittels Computersimulation auf virtuellen Streckenmodellen konzipiert und prototypisch entwickelt werden. Aspekte, die dabei Berücksichtigung finden sollen, sind Qualitätsanforderungen an das Streckenmodell hinsichtlich unterschiedlicher Sensor- und Reglerfunktionen, erforderliche Parameter/Dimensionen für die darzustellenden (Verkehrs-)Szenarien, Klassifizierung der Ähnlichkeit/Genauigkeit von digitalen Zwillingen (Simulation und Versuchsfahrzeug) oder auch eine Validierungssystematik für solch ein virtuelles Umfeldmodell.
Aufbauend auf die Anforderungen an die Simulation und den Spezifikationen an das virtuelle Streckenmodell soll ein systematisches und belastbares Verfahren zur simulationsbasierten Absicherung von automatisierten Fahrfunktion erarbeitet werden.

(Third Party Funds Single)

Abstract:

Modern driver assistance systems for self-driving cars often rely on data collected by different sensors to determine the necessary system decisions. To prevent system failures, different techniques can be used to enhance the reliability of such multi-sensor systems, e.g., aggregation, filtering, majority voting and other mechanisms for fault tolerance. As a consequence, erroneous sensing is rare but can be correlated in successive sensor readings (e.g., as error bursts) and also between sensors (e.g., in specific environmental conditions such as bad weather).

For a reliable design, error probabilities of such multi-sensor systems must be determined. In the project an existing analytical model based on Markov chain as well as a simulation model should be extended. This includes the following aspects: extensions for several correlated sensors, integration of practically relevant sensor fusion algorithms, consideration of environmental conditions, adaption of structure and parametrization of the error model, extensions of the simulation for rare events and inclusion of code, validation of the model results based on available data, and realization as a software tool for the reliability design of multi-sensor systems.

In this project, the preliminary work of the INI.FAU project is to be built and both the existing analytical model based on Markov chains and the simulation model for multi-sensor systems are to be expanded. The desired scientific knowledge consists in the further development of the analytical Markov model, which already takes into account bursts of errors of individual sensors and dependencies between two sensors, the expansion to more sensors, the consideration of further error prevention strategies and a tool implementation. Furthermore, knowledge of the use of rare event simulation is to be achieved in order to execute more detailed simulation models of multi-sensor systems in practical terms and thus to derive statistically reliable results. The simulation allows an even more realistic system simulation and a validation of the analytical modeling. A scientifically based methodology is developed to determine the reliability of multi-sensor systems.

(Third Party Funds Single)

Abstract:

Vehicles are evolving to a mobile data platform. Besides mobility as their main purpose, the demand for entertainment, connectivity and current software is increasing. Besides installing updates in the workshop there is already today a mobile communication module built into the car, by which map updates, traffic information and entertainment applications are run. Mobile communication however depends on existing network coverage and can be limited in certain areas. Additionally a fee has to be paid to the mobile network provider, which is usually dependent on the amount of used data traffic. In this project additional technologies are to be evaluated, that may enable effective communication in the future. Publicly available WLAN hotspots have a potential as they are often available in the road area and can mostly be used cost-effective. Additionally vehicles need similar information, for instance a map update, that has to be delivered to multiple cars in the field. Therefor direct communication between vehicles as in 5G offers the possibility to exchange information in the field and reduce the usage of mobile communication. The goal is to test the combination of different technologies to a complex, heterogeneous vehicle network and evaluate the applicability of opportunistic networks. At the same time, proposals for future standardization are to be developed. From a scientific point of view suitable coordination and routing mechanisms are vital as connection times are short, vehicles serve as temporary storage and source of information and effective usage of transmission paths is relevant.

(Non-FAU Project)

(Non-FAU Project)

Abstract:

In the future, data exchange will no longer take place exclusively between the cloud (or a server in a data center) and a mobile device. Instead, communication between devices will be established directly on the basis of application relationships in order to realize immersive applications, automated driving or virtual reality. To this end, 5G and future network technologies are increasingly following the data-centric paradigm in their design, in which, among other things, the increasing relevance of direct device communication is taken into account. Another elementary development also contributes to this: Computing or information resources are no longer provided exclusively by cloud servers.
Multi-Access Edge Computing (MEC) is part of current research and deals with the provision of resources on distributed edge nodes. For example, MEC instances can be located close to base stations to serve applications with special requirements, such as low latency, low-variance jitter, high bandwidth, or privacy requirements close to the end device. Over time, services will emerge whose components can be deployed literally anywhere and in a distributed manner - without the need to consider a mandatory hierarchical network topology. In addition to a cloud instance, a service can therefore also be operated on the edge instance in the vicinity, e.g. a mobile radio base station, a traffic control system or even a neighboring user equipment (UE). Multi-level MEC constellations are also possible. A homogeneous technology stack that extends cloud computing enables a data-centric architecture that can simultaneously accommodate stringent service requirements.
The resulting architecture can be viewed from two perspectives. In terms of network communication, MEC resources are accessible via only a few links or hops. This geographical or topological proximity means that the links are not overloaded, which results in the aforementioned performance advantages. With regard to the services provided, a MEC orchestrator can dynamically adapt service deployments on compute nodes to the current situation and integrate resources into the topology or remove them, for example, to save energy. In addition to orchestration decisions, the movement of nodes also leads to a change in the network topology. In order to exploit the full potential of MEC and thus also to be able to operate services that rely on MEC resources, both perspectives must be combined in a meaningful way.
In static environments, MEC resources can usually be planned well in advance. It becomes a challenge especially when the mentioned dynamic topology changes or mobility of UEs affect the overall system. One of the key questions that arises is: Can the communication requirements of MEC-dependent services, which are necessary for the smooth implementation of the service, be met at all times?
The research project deals with the selection of the best MEC resources, for example from a UE point of view, as well as the, from a network point of view, best locations for orchestrators to provide the services. In particular, the focus is on the current network and topology situation in combination with the strict communication requirements of services that need MEC resources. Strategies and algorithms, for example based on graphs, are developed, implemented and evaluated. Verification takes place through system-level simulations and real-world deployment.