The VDI Autonomous Driving Challenge (ADC) is a competition for talent in the field of autonomous driving. At this year's challenge at the DEKRA Lausitzring, the team from Bochum University of Applied Sciences took second place in the final against München University of Applied Sciences with the “Mercedes BOnz” vehicle.

After taking first place in 2023 with their then-current vehicle, “BOdge”, the team was able to perform successfully again this year, gaining valuable practical experience in the field of autonomous driving.

Further information can be found here.

The project is dedicated to the development of a sustainable online shop that combines technical implementation, marketing and strategic planning. In interdisciplinary teams, market analyses are carried out, digital business models are developed and sustainable strategies are devised. This is complemented by the implementation of innovative products, the optimisation of digital marketing and the examination of topics such as business planning and funding, financing. The aim is to create practical solutions that combine e-commerce, sustainability and entrepreneurship.

More information and pictures will be available soon.

The module covers the basics and techniques of supervised and unsupervised machine learning. Among others, NumPy, sklearn and Keras are used.

• Professor: Prof. Dr. Jörg Frochte
• Bachelor or Master: Bachelor
• Study fields: Computer Engineering, Mechatronics & IT
• Open for optional module: Yes
• Previous knowledge: Basic knowledge of Python, as well as basic mathematics from studies for computer scientists/engineers.
• Link to study program: 
  - Computer Engineering
  - Mechatronics & IT

The module provides an overview of data-driven decision making using machine learning methods. Basics of Python, data preprocessing as well as modeling are introduced and applied in practical exercises using sample data.

• Professor: Prof. Dr.-Ing. Christian Bockermann
• Bachelor or Master: Bachelor
• Study fileds: BWL/VWL, W-Ing, IBM and Business Informatics
• Open for optional module: Yes
• Previous knowledge: Basic knowledge of statistics and programming is helpful
• Links to the study program:
  - Business Administration
  - Business Information Systems
  - International Business and Management

The module covers (Deep) Reinforcement Learning and Evolutionary Learning. Among others, Python, Keras and OpenAI Gym are used.

• Professor: Prof. Dr. Jörg Frochte
• Bachelor or Master: Master
• Study fields: Computer Engineering
• RMS / open for optional module: Yes
• Previous knowledge: Basic knowledge in supervised learning i.W. neural networks.
• Link to degree program:
  - Computer Science

In the module, topics of probability, planning, decision making, supervised and unsupervised machine learning, and optimal control are covered with reference to mobile robotics.

• Professor: Prof. Dr.-Ing. Daniel Schilberg
• Bachelor or Master: Master
• Study fields: Mechanical Engineering, Mechatronics
• RMS / open for optional module: Yes
• Previous knowledge: /
• Link to degree program:
  - Mechanical Engineering
  - Mechatronics

Among other things, the module addresses the question "What is AI?". Further components are: Introduction Python and Keras, (Un-)Supervised Learning, Reinforcement Learning, GAN and Machine Learning Basics.

• Professor: Prof. Dr.-Ing. Stefan Müller-Schneiders
• Bachlor or Master: Bachelor
• Study fields: Computer Science
• Open for optional module: No
• Previous Knowledge: Programming, mathematics 1 and mathematics 2
• Link to degree program:
  - Computer Science

Skills for handling large and unstructured data sets and their efficient analysis will be learned. For this purpose, data from real sensors, mobile devices and from open-data sources, and also taking into account ethical and legal aspects, will be used. Big Data application scenarios and processing techniques, infrastructures and ecosystems for big data analysis (including MapReducc techniques and Apache Hadoop) will be demonstrated. Fundamentals of NOSQL database systems as well as modern concepts of distributed data management will be discussed. Exploratory and structuring analysis methods, including data visualization and machine learning-based techniques, will be applied.

• Professor: Prof. Dr. rer. nat. Henrik Blunck
• Bachelor or Master: Master
• Study fields: Computer Science, Electrical Engineering
• RMS / open for optional module: Yes
• Previous knowledge: /
• Link to degree program:
  - Computer Science
  - Electrical Engineering

The module explores the introduction to autonomous driving (Level), sensors / sensor data fusion, computer vision and introduction to Carla.

• Professor: Prof. Dr.-Ing. Stefan Müller-Schneiders
• Bachelor or Master: Master
• Study fields: Computer Science
• Previous knowledge: Bachlormodules Computer Science
• Link to the study program:
  - Computer Science

Context-aware Computing can be understood both as a sub-discipline and as an application domain of AI, and is primarily concerned with the design and realization of intelligent assistants that automatically perceive, interpret, and appropriately respond to contexts and context changes.

• Professor: Prof. Dr. rer. nat. Henrik Blunck
• Bachlor or Master: Bachelor
• Study fields: Computer science, as elective for Electrical Engineering
• Open for optional module: Yes
• Previous knowledge: Programming in Java
• Link to degree program:
  - Computer Science
  - Electrical Engineering

The module focuses on simultaneous localization and mapping with a ROS-based mobile robot.

• Professor: Prof. Dr.-Ing. Daniel Schilberg
• Bachlor or Master: Master
• Study fields: Mechanical Engineering, Mechatronics
• RMS / open for optional module: Yes
• Previous knowledge: Basic knowledge in Python
• Link to study program:   
  - Mechanical Engineering
  - Mechatronics

In the module, additional to the basics of quantitative Water Management, competences for the analysis of Water Management data are given. Matlab and R are used.

• Professor: Prof. Dr.-Ing. Christoph Mudersbach
• Bachlor or Master: Master
• Study fields: Civil Engineering, Environmental Engineering
• RMS / open for optional module: No
• Previous knowledge: Basic knowledge of hydromechanics and hydrology
• Link to study program:
  - Environmental Engineering

In the module, the different aspects of digitization in industrial practice are examined - from the software used (ERP, MES, CAD/ CAM) to the analysis of data (Exploartive Visualization, Machine Learning, AI) to concrete practical applications in the production and logistics environment.

• Professor: Prof. Dr. Andreas Merchiers
• Bachlor or Master: Bachelor
• Study fields: Industrial Engineering and Management, Civil Engineering, Mechanical Engineering, Electrical Engineering, Business Informatics
• RMS / open for optional module: No
• Previous knowledge: -
• Link to study program:
  - Business Informatics
  - Industrial Engineering

In the module, current challenges and approaches to solutions in the areas of production and logistics are developed. One focus is on the area of digitalization.

• Professor: Prof. Dr. Andreas Merchiers
• Bachlor or Master: Master
• Study fields: Master of International Management (MIM)
• RMS / open for optional module: Yes
• Previous knowledge: -
• Link to study program:
  - International Management