2. Deep Learning Day

Machine Learning for Autonomous Driving

By Dr. Michael Hermann / Dr. Bastian Bischoff, Robert Bosch GmbH


All recordings from the 2. Deep Learning Day

About the 2. Deep Learning Day

On January 12th
i003 (Audimax)
Liebe Kollegen, liebe Studierende,

ich möchte Sie herzlich zum 2. Deeplearning an der Hdm einladen. Die Veranstaltung findet am 12.01.2018 ab 13.30 im Hörsaal i003 (Audimax) statt.

Deeplearning ist derzeit eines der populärsten Themen in der Informatik. Die Performance von Anwendungen wie der Objekterkennung, Spracherkennung, Automatischer Übersetzung ... wurde durch tiefe neuronale Netze erheblich verbessert. Noch spannender aber ist das nahezu unbegrenzte Feld von neuen intelligenten Anwendungen, die durch tiefe neuronale Netze ermöglicht werden. Hierzu zählen z.B. die automatische Generierung von Text, Bildern, Audio usw.

Die Vorträge am Deeplearning Day vermitteln:

- einen Einblick in die aktuelle Forschungstrends

- Algorithmen

- Anwendungen, vornehmlich im Umfeld Autonomes Fahren

- Architekturen für komplexe, hochperformante KI-Systeme


13.30h: Welcome (Prof. Dr. Johannes Maucher, HdM)

13.45h: Understanding the world by learning how to model it (Johannes Theodoridis, M.Sc., HdM) - Artificial Intelligence is on everyone’s lips. But how “intelligent” are our AI systems really? What is resilient about the current hype, where do we stand and what can we expect? A deep dive into the latest developments in AI and machine learning.

14.45h: Machine Learning for Autonomous Driving (Michael Herman, Dr. Bastian Bischoff, Robert Bosch GmbH, Differentiating AI - Environmental Understanding - Decision Making) - For bringing autonomous vehicles on public roads, many questions need to be answered. For example, what are essential features in the high-dimensional observations from varying sensors, what are other traffic participants going to do, how are they going to react to the behavior of the autonomous car, how should an autonomous system behave, or how to find optimal strategies according to these criteria, while guaranteeing safety requirements. Machine Learning approaches can be used to answer these questions to some extent. While Deep Neural Networks have outperformed traditional methods in various applications, they have also shown to be vulnerable against adversarial perturbations on problems with high-dimensional input spaces. Since this can prevent its usage in safety- and security-critical applications such as autonomous driving, it is necessary to both understand the limits of these models and to increase their robustness.

15.45h: Applications of Deep Learning in Autonomous Driving (Eike Rehder, Daimler AG, R&D – Environment Perception) - Deeplearning is considered one of the enabler technologies for automated driving. Due to the cognitive versatility of neural networks, they can be applied in nearly every component of automated driving. In this talk, we will demonstrate examples of perception, situation interpretation and decision making for automated vehicles.

16.45h: Cognitive Systems (Dr. Thilo Maurer, IBM Power Acceleration and Design Center) - IBM classifies applications of artificial intelligence, expert algorithms and robotics as cognitive systems. This class differs significantly from traditional IT applications in that it also uses unstructured data such as images, text, speech and sensor data. Economical processing of such thin data has only become possible in recent years due to the progressive reduction in the price of computing units. In this context, methods oriented to the functioning of the human brain are increasingly being used. The lecture will also discuss the IBM Watson Software, current machine learning hardware, and the SyNAPSE research project, and how these approaches fall under artificial intelligence.

Mehr Information zu den Vortragsinhalten finden Sie hier:


Ich freue mich auf Ihre Teilnahme.

Herzliche Grüße

Johannes Maucher