Analysis and evaluation of methods for extracting vital and biofeedback data from driver-related video data

Topic Area

The aim is to establish an evaluation of various methods that can be used to extract vital signs and biofeedback data from video data of drivers. The extensive, interdisciplinary research with methods including such as PPGI (Photoplethysmography Imaging) for heart rate measurement. The aim is to find various methods that provide vital parameters as comprehensively as possible and to evaluate them in the context of the driver. In addition to detailed documentation, the aim is also to implement individual procedures for demonstration purposes

Task

- Detailed and interdisciplinary research into the required basic principles and known procedures - Evaluation of the various possible procedures according to - their requirements for the necessary source video data, - their limitations and accuracies, - feasibility and implementability in the context of driver and vehicle - Implementation of individual procedures for demonstration purposes - Testing and validating the individual procedures - Detailed evaluation, interpretation and documentation of the results

Knowledge

- Desired degree program and study focus: Data Science, Computer Science, Medical Engineering, Medical Informatics, Digital Medicine or comparable - Language skills: German and English - (A) Good programming skills (Python) - (A) Good experience in data maintenance and visualization - (B) Basic knowledge of analyzing and processing video data - (B) Basic knowledge of app development (Android) helpful - Soft skills: - (A) Independent and structured way of working as well as communication and teamwork skills - (A) High level of motivation and willingness to work independently - (B) Quick comprehension and strong conceptual skills

Contact Person

Baran Guel

Conceptualization of a reinforcement learning approach for human-assisted root cause analysis in software-defined systems

Topic Area

Modern system development is characterized by increased customer requirements and greater market and time pressure. The innovations required for this are created on the one hand by a higher proportion of software in products and on the other hand by the networking of more and more previously independent systems, resulting in heterogeneous and therefore more complex IT structures overall. This is also reflected in the automotive industry, where new business models are being developed via software-defined vehicles. Modern E/E architectures enable the vehicle to communicate with its environment as well as collect data during operation, which can then be used by manufacturers to improve driving or comfort services. To realize such a data loop, automated analysis of the software in operation is key. A key challenge here is to link the events occurring in the system in order to be able to determine the cause of any errors. Conventional approaches fail to take into account temporal behavior as well as contextual information, which can be provided by a system engineer. Therefore, a reinforcement learning approach is to be developed in the context of this thesis, which can incorporate the knowledge of the system engineer as well as information about updates of the system into the automated linkage.

Task

- Analysis of existing approaches for root cause analysis - Development of an own approach in the context of a software-defined DevOps environment - Integration into an analysis platform for distributed cloud systems - Evaluation of the approach using an own data set and comparison with conventional methods

Knowledge

- Very good conceptual skills - Prior knowledge of deep learning and Markov chains - Basic knowledge of software engineering and IT systems - Good programming skills - Very good English skills

Contact Person

Matthias Weiss

Conceptualization of a reinforcement learning approach for self-adaptive anomaly detection in software-defined systems

Topic Area

Modern system development is characterized by increased customer requirements and greater market and time pressure. The innovations required for this are created on the one hand by a higher proportion of software in products and on the other hand by the networking of more and more previously independent systems, resulting in heterogeneous and therefore more complex IT structures overall. This is also reflected in the automotive industry, where new business models are being developed via software-defined vehicles. Modern E/E architectures enable the vehicle to communicate with its environment as well as collect data during operation, which can then be used by manufacturers to improve driving or comfort services. To realize such a data loop, automated analysis of the software in operation is key. In order to detect changes or emerging errors at an early stage, the incoming data must therefore be continuously analyzed for anomalies. A particular challenge is posed by the high system dynamics, which means that the anomaly detection methods used must be continuously updated in order to always be able to issue reliable alarm messages. Since this has so far been associated with a high manual effort, a self-adaptive approach is to be developed within the scope of this work, by means of which suitable anomaly detectors can be selected and configured automatically on the basis of the system properties.

Task

- Analysis of existing approaches for self-adaptive anomaly detection - Development of an own approach in the context of a software-defined DevOps environment - Integration into an analysis platform for distributed cloud systems - Evaluation of the approach using an own data set and comparison with conventional methods

Knowledge

- Very good conceptual skills - Prior knowledge of deep learning and signal processing - Basic knowledge of software engineering and IT systems - Programming skills in Python - Very good English Skills

Contact Person

Matthias Weiss

Systematic testing of AI-based systems

Topic Area

Testing of AI-based systems such as autonomous vehicles is challenging due to many situations and scenarios. Brute force is expensive and has gaps, as we see in practice. We thus use synthetic data for an AI-driven testing. This data covers real-world scenarios to train autonomous systems in a simulation-based environment. The training success is evaluated in a data loop and enhanced to close blind spots and unknown knowns. This thesis targets to integrate a requirements and test engine to an automated test system.

Task

The goal of the thesis is to integrate existing parts of the system. A fully running system shall be implemented. The integration comprises verification and validation checks for the existing parts. Professional tools such as DOORS shall be used for industry-scale AI-based testing of autonomous systems.

Knowledge

Knowledge in Python Industry-scale software engineering and tools Work in a self-independent way Passionate about clean and good quality code Capable of integrating your work with other parts of the system

Contact Person

Christof Ebert

Design and implementation of a software complexity assistant system using Digital Twin

Topic Area

The aim of the project is to identify the different drivers of complexity within this project and quantify it with appropriate measures. We focus on the change of the software part. Specifically, we want a methodology that predicts the complexity of changing a given software module. In order to assist the management of the Digital Twin, an assistant system is to be created, which assesses the software complexity based on the described aspects. The feasibility of the assistant system will be evaluated on the software stack of the Digital Twin. Since we will have five different implementations of the same problem from the lab courses, there will be test data available to check the assessment results for plausibility.

Task

The Master Project should first analyze the literature for drivers of complexity and established complexity measurement methods in order to derive a methodology that identifies the complexity drivers and quantifies them. Moreover, within the project, an assistant system will be developed that assesses the complexity and visualizes the results. The assistant system will be evaluated using different variants of the Digital Twin.

Knowledge

Independent, scientific work Very good math and programming skills Good English skills

Contact Person

Golsa Ghasemi