Artificial Intelligence and Machine Learning for ADAS and ADS - Introduction and Basics
Description
The functions of automated driving - no matter what degree of automation - usually require the application of modern artificial intelligence techniques in order to be able to realize the desired functionalities at all. The aim of this seminar is to present the basic methods of Artificial Intelligence and Machine Learning. The methods should be demonstrated with concrete examples from the fields of assisted and automated driving. Care is also taken about validation, verification and safeguarding of the related models and AI-based software components.
Content
- Introduction of data-based development versus analytical and rule-based approaches
- Overview of the different procedures and areas of application
- Artificial Neural Networks, Deep Learning, various variants and architectures
- Decision and regression trees
- Support Vector Machines
- Validation and safeguarding of models, sampling procedures, robustness assessment
- Data preparation and problem parameterization
- Meta modeling and model committees
Objectives
This seminar provides an overview and a brief introduction to the relevant methods of Artificial Intelligence and Machine Learning, so that both developers and managers can clearly decide which methods and procedures are relevant for their applications and which possible pitfalls they should consider in the application.
Who should attend?
Developers and (project) managers who have not yet had deep experience with the methodology and want to get a quick overview and introduction to the use of artificial intelligence.
Dates & Registration
If you are interested in this seminar please click here.
Instructors
Andreas Kuhn
Andata Entwicklungstechnologie GmbH
Dr. Andreas Kuhn studied Technical Mathematics and Mechanical Engineering at the Technical University of Vienna. After his dissertation on the simulation of special satellite formations for the European Space Agency, he began his professional career in crash simulation at BMW. After further years as a consultant for stochastic simulation at EASI Engineering GmbH (today carhs), he founded ANDATA in 2004, where he is responsible for development and research as managing partner. Since 2009 he has also been co-owner of Automotive Safety Technologies GmbH in Gaimersheim. His professional interests are founded in effective and efficient development, validation and assessment methods for complex, safety-critical systems. In particular, he has been working for more than 20 years on the development and combined application of methods from the fields of artificial intelligence, machine learning, advanced simulation methods, scenario-based approaches and according process models in the virtual development of vehicles and autonomous robots. His current activities are the development and implementation of cooperative, networked, automated driving strategies for effective traffic automation.