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.
- 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.