The BeIntelli project led by the DAI-Lab serves as a showcase project for the AI-driven autonomous mobility of the future. Based on real scenarios, the mobility solutions of the future are tested and evaluated in the BeIntelli real-lab in the heart of Berlin. One particular focus lies on showcasing connected, cooperative automated mobility (CCAM). We take a top-level view on the entire infrastructure by communicating with digitized vehicles, the infrastructure (Edge) and cloud. On that foundation, we develop targeted services for showcasing such as Smart Parking.

To optimize the use of the available space allocated for parking more efficient, different solutions were developed. For example vertical parking infrastructures, e.g., multi-storey car parks, elevator-based parking stacks etc. solutions are used and dynamic pricing solutions. With this final thesis, we want to develop a software solution – Efficient Parking Service (EPS) – with a main focus on street side row parking (secondary parallel parking) based on the BeIntelli concept and CCAM-enabled vehicles.

The thesis is designed as a master thesis. In case you fit this topic excellently, it can be restructured as a bachelor thesis.

Main Research Focus and Tasks

• Conduct literature research and create an overview on current Smart Parking solutions for Efficient Parking.
• Develop and sketch the processes for a platform-based EPS for vehicle, edge and cloud, preferably with Business Process Modeling and Notation (BPMN).
• Develop the planned EPS in a simulation environment for proof of concept.
• Define and discuss the minimum requirements for Efficient Parking enabled vehicles.
• Conclude with a discussion on benefits and disadvantages of possible future scenarios.

Prerequisites

• General knowledge on state-of-the-art autonomous mobility solutions are beneficial
• Experience with communication frameworks and networking
• Experience with Simulation of Urban Mobility (SUMO) or CARLA
• Experience with practical process modeling (for example BPMN) is beneficial
• Programming skills in Python required, additional languages are beneficial
• High motivation of discovering novel use cases within the autonomous mobility framework

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.

Time Series such as sensor signals, speech or stock prices generally vary in length and speed. To cope with such temporal variations, warping distances such as Dynamic Time Warping (DTW) are often used. In recent years, the problem of finding an average of time series under DTW has been faced and many properties such as the existence, uniqueness, complexity, exact solutions and heuristic solutions have been studied. In practice, a DTW average may not 'look like' an averagely shaped curve of the sample time series. This is reasoned in the nature of the DTW distance itself. Therefore, several alternative warping distances have been proposed. However, as of today, they have rarely been used for computing average curves. The goal of this thesis it to explore the behavior of different warping distances for time series averaging. One challenge is to evaluate such averages quantitatively and qualitatively.

Prerequisites

• Good programming skills (Python, Matlab or Java)
• Solid basics in Analysis and Linear Algebra, ideally experience with gradient descent optimizers
• Interest in theoretically oriented research

Literature

• Schultz, D., & Jain, B. (2018). Nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces. Pattern Recognition, 74, 340-358.
• Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern recognition, 44(3), 678-693.
• Zhao, J., & Itti, L. (2018). shapedtw: Shape dynamic time warping. Pattern Recognition, 74, 171-184.
• Keogh, E. J., & Pazzani, M. J. (2001, April). Derivative dynamic time warping. In Proceedings of the 2001 SIAM international conference on data mining (pp. 1-11). Society for Industrial and Applied Mathematics.
• Cuturi, M., & Blondel, M. (2017, July). Soft-dtw: a differentiable loss function for time-series. In International Conference on Machine Learning (pp. 894-903). PMLR.
• Marteau, P. F. (2008). Time warp edit distance with stiffness adjustment for time series matching. IEEE transactions on pattern analysis and machine intelligence, 31(2), 306-318.
• And own literature research

Geodesically convex optimization has recently attracted attention from the machine learning community due to the realization that some important problems that appear to be non-convex at first glance, are geodesically convex, if we introduce a suitable differential structure and a metric. In this thesis, the student is expected to conduct an empirical study on the performance of geodesic convex optimization methods for applications in machine learning, compared to the state-of-the-art non-convex optimization techniques.

Prerequisites

• English language
• successfully completed courses Machine Learning I and II
• Programming skills in Python
• Knowledge in Differential Geometry and Convex Optimisation

The Electrical Grid plays an important role in our everyday life. As to bring the new technologies in place and facilitate the energy transition, Simulations have been conducted to assess the feasibility of new developments. Today we do not only talk about the stability, reliability and efficiency of the grid but also its resilience or the ability to Anticipate, React, Adapt, and Recover. Therefore, to increase the resilience of today’s power grid, simulation and software assessments are needed to couple the multidomain inter-dependencies of the Cyber-Physical Human Systems (CPHS). Extreme weather events have been occurring more frequently around the world. These events demonstrated how vulnerable the distribution grid is.

Well-placed and coordinated enhancements, such as system hardening and redundancy, orchestrated microgrids, and advanced fault detection, can reduce the number of outages that could occur due to High Impact Low Probability (HILP) events. We offer a Bachelor / Master Thesis in the field of Power Grid Simulation and integration of Distributed Energy Resources. The thesis will encapsulated the different stages from Model Development to Vulnerability Assessment, and Power Flow Optimization related to Distributed Energy Resources in Power Networks.

Requirements

• Studies in the fields of technical informatics, control engineering, automation, electrical engineering, energy engineering, or similar
• Good knowledge and practical experience in python, co-simulation paradigms, ArcGIS, Simulink or Power Factory
• Teamwork and Goal Oriented
• Interest in Renewable Energy Topics and Automation Technology
• Task Oriented and Independent Learning

research area