Abschlussarbeiten

Das DAI-Labor bietet hier zu mehreren seiner Forschungsschwerpunkte mögliche Abschlussarbeiten zum Bachelor of Science (BSc) / Master of Science (MSc). Für nähere Informationen und Hinweise zu den einzelnen Themen kontaktieren Sie bitte den jeweiligen Betreuer.

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Description

The "BeIntelli" project led by the DAI-Lab serves as the AI showcase for the mobility of the future. Based on real scenarios, the mobility solutions of the future are directly tested and evaluated. A particular focus lies on the use of distributed intelligent systems that interact with each other and thus generate significant synergies.

Within the framework of this project, our platform team is now offering final theses. We take a top-level view on the entire infrastructure by communicating with digitized vehicles and the infrastructure (Edges). On that foundation, we develop targeted services such as parking recommendations, user behavior predictions or hazard mitigation support by using AI-based methods.

Main Research Focus

  • Conduct literature research and create an overview on current platform-based solutions.
  • Develop a research question related to a novel approach of solving a problem in the field of digitized infrastructure that can communicate with vehicles and the cloud.
  • Find suitable open-source data sets that could be used for systematically developing a solution that answers the research question.
  • Include several methods that could solve the defined problem (such as state-of-the-art neural networks, federated learning, etc.), enhance their approaches and compare their performances.
  • Conclude with a discussion on benefits and disadvantages of possible future scenarios.

Prerequsites

  • General knowledge on state-of-the-art autonomous mobility solutions and the platform economy concept are beneficial.
  • Preferably successfully completed university courses in the area of AI and Neural Networks.
  • Programming skills in Python, additional languages are beneficial.
  • High motivation of discovering novel use cases within the autonomous mobility framework.
  • Knowledge of Deep Learning Frameworks, such as Tensorflow/Keras.
  • Good grades in relevant fields are beneficial.

Curious? Get in touch.

We are looking forward to receiving your application. Please include your topic proposal, current transcript of records and CV in tabular form; all documents in PDF format.

Ansprechpartner
Plattform Economy and Autonomous Mobility Solutions: Data-Driven Network Effects
Emerging Technologies for Data-Driven Network Effects within Digitalized Urban Infrastructures and towards Autonomous Driving Applications
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Level: Master Thesis

Emerging Technologies for Data-Driven Network Effects within Digitalized Urban Infrastructures and towards Autonomous Driving Applications

Description

Mobility is changing. Technological innovations introduced to the market, the digitalization of infrastructures and services, or social challenges such as the COVID-19 pandemic are influencing the way we understand and experience mobility. By providing novel services like MaaS and carpooling, autonomous vehicles are set to take the centre stage for new mobility. Given this development, even more disruptive technologies and use cases will emerge. V2X connectivity and smart infrastructure leverage services in the area of multimodal transport, traffic, maps & navigation, parking, safety and payment. Those innovations create room for novel HMI solutions, new mobility solution providers and many other potential actors. 

The DAI-Lab is researching and developing solutions that could meet the requirements of an cooperative, connected and automated mobility landscape (CCAM). An essential focus is to understand the role and effects that appear from data collected in digitalized infrastructures and autonomous vehicles. These are linked to mobility services offered in these environments.

Given the concept of Platform Economy (PE), it is very interesting to investigate data-driven network effects (DDNE). DDNE occurs when a product improves significantly by shared data generated by its users, and is often enhanced by AI technologies. This thesis’ target is to conduct research on such effects in the context of PE in CCAM use cases.

Main Research Focus

  • Conduct literature research and create an overview on possible interplay of cooperative and connected autonomous mobility framework (CCAM) and platform economy (PE) approaches.
  • Choose several related use cases, identify their relevant stakeholders and visualise the interconnectivity between all instances.
  • Analyse the impact of chosen use cases in terms of data-driven network effects (DDNE) and develop a framework/tool that provides answers to the following questions:
    • What are DDNE in the context of digitalized urban infrastructures and autonomous driving? 
    • How can digitised urban infrastructure enrich the data landscape qualitatively?
    • What properties do the different actors and entities show (autonomous vehicle, pedestrians, cyclists, Road-Side-Units, etc.)?
    • Given the use cases, what data is needed, what data is helpful? How can different data be gathered and prioritized for the given use cases? 
    • What challenges occur in the context of gathering the data? How can they be solved?
    • What role does data security regulation and customer’s demand in Germany/EU play? How could developments in this domain inhibit or amplify DDNE?
  • Conclude with a discussion on benefits and disadvantages of possible future scenarios.

Prerequisites:

  • General knowledge on state-of-the-art autonomous mobility solutions and the platform economy concept.
  • Preferably successfully completed university courses in the area of platform economy.
  • High motivation of discovering novel use cases within the autonomous mobility framework and DDNE.
  • Good grades in relevant fields are beneficial.

References

Curious? Get in Touch.

We are looking forward to receiving your application. Please include your current transcript of records and Curriculum vitae in tabular form; all documents in PDF format.

Ansprechpartner
Marc Guerreiro Augusto
marc.augusto@dai-labor.de
Plattform Economy and Autonomous Mobility Solutions: Effects on Urban Environment
Explaining the Effects of Autonomous Driving in Platform-based Urban Mobility on Public Space, Road Infrastructure and MaaS Solutions
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Level: Master Thesis

Explaining the Effects of Autonomous Driving in Platform-based Urban Mobility on Public Space, Road Infrastructure and MaaS Solutions

Description

In recent years, the development of cooperative and connected automated mobility solutions (CCAM) has experienced an upswing. Platform based services such as Mobility-as-a-service (MaaS), Car-as-a-service (CaaS), car pooling, ride hailing, and many more are leading to various changes on how mobility is perceived. Today, the question arises of how holistic approaches can be designed to deploy CCAM use cases into real-world environments. 

Thus, an essential focus is to understand the role and effects that appear from data collected in digitalized infrastructures and autonomous vehicles. Digital platforms acquire large amounts of data from various sources, which are aggregated and then made available for use in a wide variety of products and services. Consequently, the availability of this data shapes the success of mobility services offered in CCAM-environments. Given this foundation, several effects on the city landscape are possible. In fact, an important research topic is the effect of (steered) data on public space, road infrastructure and MaaS solutions.

Main Research Focus

  • Conduct literature research and create an overview on possible interplay of cooperative and connected autonomous mobility framework (CCAM) and platform economy (PE) approaches.
  • Contribute your own profound ideas.
  • Choose several CCAM use cases, identify relevant Stakeholders and visualise the interconnectivity between all instances.
  • Analyse the impact of chosen use cases in terms of the city landscape and develop a framework/tool that provides answers to the following questions:
    • To what extent can MaaS solutions improve efficiency for end users?
    • How do CCAM solutions impact land use? 
    • How could CCAM and MaaS alter the design of urban space?
    • How does CCAM impact CO2 emissions and km travelled per day?
    • What further parameters need to be taken into account?
  • Conclude a recommendation how to steer CCAM development towards achieving a desirable city of the future

Prerequisites

  • General knowledge on state-of-the-art autonomous mobility solutions and the platform economy concept.
  • Preferably successfully completed university courses in the area of business modelling and platform economy.
  • You show a high degree of motivation in assessing CCAM impacts on several dimensions in order to shape the desirable city of the future. 
  • Good grades in relevant fields are beneficial.

References

Curious? Get in Touch.

We are looking forward to receive your application. Please include your current transcript of records and Curriculum vitae in tabular form; all documents in PDF format.

Ansprechpartner
Marc Guerreiro Augusto
marc.augusto@dai-labor.de
Plattform Economy and Autonomous Mobility Solutions: Business Model Development
Autonomous Mobility Data Platform: Value Proposition, Governance and Business Model for the Platform Economy Framework
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Level: Master Thesis

Autonomous Mobility Data Platform: Value Proposition, Governance and Business Model for the Platform Economy Framework

Description

Observing our changing mobility landscape, new innovative solutions within the field of autonomous driving are emerging everyday. Platform based services such as Mobility-as-a-service (MaaS), Car-as-a-service (CaaS), car pooling, ride hailing, and many more are leading to various changes on how mobility is perceived. As vehicles are often no longer seen as something to be owned, but more as a service, related platforms are getting more popular everyday. 

This leads to a new set of challenges to involved actors, such as OEMs, start ups, public transport and governments. Namely, all stakeholders active in the mobility domain have to adapt to changing needs of the end users to make the travel experience safe and pleasant. At the same time, challenges such as sustainability (e.g. carbon footprint) need to be taken into consideration. 

Given this status quo, the necessity of novel business models is a logical consequence. However, due to the large variety of important components, the partly hard-to-reform landscape and the diversity of involved stakeholders makes this an interesting challenge of research. Thus, this thesis provides the opportunity of discovering what is needed for a deployment of novel applications and services within the area of autonomous mobility.

Main Research Focus

  • Conduct literature research and create an overview on possible interplay of cooperative and connected autonomous mobility framework (CCAM) and platform economy (PE) approaches.
  • Contribute your own profound ideas.
  • Choose several PE use cases, identify relevant Stakeholders and visualise the interconnectivity between all instances.
  • Assess what each selected case’s value proposition is, and how they could be integrated into a business model, e.g. by starting with a Business Model Canvas (BMC).
  • Develop a description of the encountered landscape, and discuss possible future scenarios including its pros and cons within the most relevant fields.
  • Bonus: include concepts for data and infrastructure security.

Prerequisites

  • General knowledge on state-of-the-art autonomous mobility solutions and the platform economy concept.
  • Preferably successfully completed university courses in the area of business modelling and platform economy.
  • High motivation of discovering novel use cases within the autonomous mobility framework.
  • Good grades in relevant fields are beneficial.

References

Curious? Get in Touch.

We are looking forward to receive your application. Please include your current transcript of records and Curriculum vitae in tabular form; all documents in PDF format.

Ansprechpartner
Marc Guerreiro Augusto
marc.augusto@dai-labor.de
Plattform Economy and Autonomous Mobility Solutions: Data Manifesto
Conception of a Data Manifesto for AI-driven Autonomous Mobility Platforms in the Platform Economy Framework
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Level: Master Thesis

Conception of a Data Manifesto for AI-driven Autonomous Mobility Platforms in the Platform Economy Framework

Description

With the rise of new digital mobility platforms, innovative services will be introduced. Platform-based services such as Mobility-as-a-service (MaaS), Car-as-a-service (CaaS), carpooling, ride-hailing, autonomous driving applications and many more are leading to various changes in how mobility is perceived. These mobility solutions are data-driven. Requirements for data acquisition, processing and use thus need to be understood and described in accordance with data laws which in turn may allow or limit the use of methods and models, respectively.

When building such solutions, it is important to deeply think about how to use data offered on platforms. This connects to detailing what kind of data is created and used, as well as explicitly not used. Addressing questions such as network effects produced with these platforms and describing the intertwining of provided data, models and services are key aspects for creating a data manifesto. 

This research focuses on building an understanding of a data manifesto for a data mobility platform in the realm of autonomous mobility. The work aims to shed light on the state-of-the-art described in the literature, restrictions other platforms self-impose, discussion on the virtues of certain self-imposed restrictions for a platform that is based in EU or Germany based on today's social discourse, and proposes a data manifesto which considers elaborated properties for allowing autonomous and platform-driven mobility solutions to unfold.

Main Research Focus

  • Conduct Research on Platform Economy fundamentals, such as Digital Platforms, Network-Effects, Multi-sided markets, etc.
  • Transfer the theory to the context of autonomous mobility by assessing the concepts of Mobility as a Service and others Use Cases.
  • Also look at data protections laws and developments in the relevant areas.
  • Conclude a manifesto that clarifies the target of data acquisition and processing

Prerequisites

  • Strong motivation to conduct research on a interdisciplinary level between Platform Economy and Autonomous Mobility
  • High interest in digital platforms, data regulation and in that context the impact of data organization on different stakeholders
  • Successfully completed university courses on state-of-the-art autonomous mobility solutions are a plus
  • Very good grades in relevant fields are desired

References

Curious? Get in touch.

We are looking forward to receive your application. Please include your current transcript of records and Curriculum vitae in tabular form; all documents in PDF format.

Ansprechpartner
Marc Guerreiro Augusto
marc.augusto@dai-labor.de
Plattform Economy and Autonomous Mobility Solutions: Key Characteristics
Modeling and Mapping Key Characteristics of Autonomous Mobility Solutions within the Platform Economy Framework
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Level: Master Thesis

Modeling and Mapping Key Characteristics of Autonomous Mobility Solutions within the Platform Economy Framework

Description

Autonomous mobility solutions are gradually making their way to both urban and rural infrastructures, paving a new path towards innovative solutions. By providing novel services like MaaS and carpooling, autonomous vehicles are set to take the center stage for new mobility. Given this development, even more disruptive technologies and use cases will emerge. With the smart urban infrastructure consisting of a wide variety of sensors and fast interconnectivity (5G), diverse and abundant data sources will be available for research, practice and policy maker. V2X connectivity and smart infrastructure leverage services in the area of multimodal transport, traffic, maps & navigation, parking, safety and payment. Those innovations create room for novel HMI solutions, new mobility solution providers and many other potential actors. 

Thus, connecting those diverse stakeholders involved in those mobility solutions will also play an important role for a successful transformation. Namely. it will be vital for platform-based mobility solution providers to understand the needs of not only the end user, but also those of other similar providers. A mapping of different platform solution providers may create new paradigms and solutions within mobility. 

In order to demonstrate the practicability of such approaches, a systematic review encapsulating different emerging disruptive technologies is desired, including use cases and regulations that emerge with cooperative, connected and automated mobility (CCAM). Analyzing key features and modeling processes of selected use cases such as vehicle on demand, smart parking or GLOSA services, by including their functioning within respective stakeholders involved will be a key to develop and embed autonomous mobility solutions in our everyday lives.

Main Research Focus

  • Conduct literature research and create an overview on possible interplay of cooperative and connected  autonomous mobility framework (CCAM) and platform economy (PE) approaches
  • Contribute your own profound ideas
  • Choose several use cases and identify relevant Stakeholders
  • Create component-, process- and data landscape, assess their interconnectivity and relate them to important actors
  • Visualize how the platform economy principles (network externalities, platform, services, etc.) are mapped to the selected use cases, e.g. via BPMN process landscaping
  • Conclude with a recommendation on how the status quo can be used and transformed towards deploying the selected use cases

Prerequisites

  • Successfully completed university courses on state-of-the-art autonomous mobility solutions, data platforms and the platform economy concept
  • High degree of motivation regarding the discovery of novel use cases within the autonomous mobility framework
  • Knowledge in process development is beneficial
  • Very good grades in relevant fields are desired

References

Curious? Get in Touch.

We are looking forward to receiving your application. Please include your current transcript of records and Curriculum vitae in tabular form; all documents in PDF format.

Ansprechpartner
Marc Guerreiro Augusto
marc.augusto@dai-labor.de
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Geodätische Konvexe Optimierung ist seit kurzem im Fokus der Machine Learning Forschung. Dies rührt daher, dass einige zentrale Probleme, die auf den ersten Blick nicht-konvex erscheinen, mit geeigneter Differentialstruktur und Metrik geodätisch konvex sind. In der Thesis soll der Studierende einen empirischen Vergleich anstellen zwischen nicht-konvexen Optimierungsverfahren und geodätisch konvexen Verfahren.

Voraussetzungen

  • Ausarbeitung ist in englischer Sprache anzufertigen
  • Kurse Machine Learning I and II erfolgreich bestanden
  • Programmierkenntnisse in Python
  • Grundlagen in den Bereichen Differenzialgeometrie und Konvexe Optimierung
Betreuer / Ansprechpartner
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Das Stromnetz spielt eine wichtige Rolle in unserem täglichen Leben. Um die neuen Technologien einzuführen und den Energieübergang zu erleichtern, wurden Simulationen durchgeführt, um die Durchführbarkeit neuer Entwicklungen zu bewerten. Heute sprechen wir nicht nur über die Stabilität, Zuverlässigkeit und Effizienz des Netzes, sondern auch über seine Belastbarkeit oder die Fähigkeit, vorausschauend zu handeln, zu reagieren, sich anzupassen und wiederherzustellen. Um die Belastbarkeit des heutigen Stromnetzes zu erhöhen, sind daher Simulationen und Software-Bewertungen erforderlich, um die Multidomänen-Interdependenzen der Cyber-Physical Human Systems (CPHS) zu koppeln. Extreme Wetterereignisse sind auf der ganzen Welt immer häufiger aufgetreten. Diese Ereignisse haben gezeigt, wie anfällig das Verteilungsnetz ist.

Gut platzierte und koordinierte Verbesserungen, wie z.B. Systemhärtung und Redundanz, orchestrierte Mikrogitter und fortgeschrittene Fehlererkennung, können die Anzahl der Ausfälle reduzieren, die aufgrund von HILP-Ereignissen (High Impact Low Probability) auftreten könnten. Wir bieten eine Bachelor-/Master-Thesis auf dem Gebiet der Stromnetzsimulation und Integration verteilter Energieressourcen an. Die Arbeit wird die verschiedenen Phasen von der Modellentwicklung über die Schwachstellenanalyse bis hin zur Leistungsflussoptimierung im Zusammenhang mit verteilten Energieressourcen in Stromnetzen kapseln.

Anforderungen

  • Studien auf dem Gebiet der technischen Informatik, Regelungstechnik, Automatisierung, Elektrotechnik, Energietechnik oder ähnliches
  • Gute Kenntnisse und praktische Erfahrung in Python, Co-Simulationsparadigmen, ArcGIS, Simulink oder Power Factory
  • Teamarbeit und zielorientierte Arbeitsweise
  • Interesse an Themen der Erneuerbaren Energien und Automatisierungstechnik
  • Aufgabenorientiertes und selbstständiges Lernen

Forschungsschwerpunkt

Analyse von Energiedaten
Betreuer / Ansprechpartner
M.Sc. Izgh Hadachi
izgh.hadachi@dai-labor.de