How might we use machine learning to improve safety and comfort for passengers?

In this worksession, we’ll try to explore the possibilities of machine learning and artificial intelligence and try to find answers to unsolved questions. Which sensors and machine learning models can help us to increase safety and comfort for our future customers? How can we use machine learning and user data to reduce the setup time of a shared mobility car? How do the use cases change between private ownership of the car compared to shared mobility concepts and what requirements do come with fully self-driving cars with respect to in-cabin surveillance to ensure highest comfort and safety for all our passengers?

Dr. Torsten Schön

Data Scientist Dr. Torsten Schön studied Bioinformatics in Weihenstephan where his enthusiasm for machine learning had its beginnings. After working as a Software Engineer for medical applications he earned his Ph.D. in machine learning and started working for different Data Science Startups in Munich. Today, besides he founded the non-profit organization “Munich Datageeks e.V.”, he works as a Data Scientist at Audi Electronics Venture.

Florian Haselbeck

Data Scientist During his integrated degree program at Audi, Florian Haselbeck studied mechatronics at the Technische Hochschule Ingolstadt. Afterwards, he started his career in the chassis development department as a software developer. Besides he began an extra-occupational master’s degree in computer science and IT-Management with focus on machine learning. In April 2018 he joined the pre-development of machine learning at Audi Electronics Venture, an Audi subsidiary.


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