[ASM research seminar] Talk by Olger Siebinga
Datum: 24. Oktober 2023Zeit: 10:00 – 11:00Ort: @ASM social room, https://fau.zoom-x.de/j/65568671567?pwd=SCtVeUVEdllNSnBoYmNQcGl2MmZHUT09
Olger will give a talk about his PhD research next week.
Interaction-aware autonomous driving is mostly achieved by including models of human driving behavior in autonomous vehicles. These models can predict the future actions of human traffic participants, and based on this information, the autonomous vehicle can better handle interactions in traffic. However, many of these approaches are based on the concept of Game Theory and therefore make strong assumptions about human behavior. For example, the assumptions that humans are rational, and do not communicate. In this talk, I will present an alternative approach to modeling human behavior in traffic interactions. We will discuss how perceived risk is a driving factor behind human behavior in non-interactive scenarios, and how communication plays an important role in interactions. Combined with Simon’s ideas of bounded rationality and satisficing, these concepts led to our novel modeling approach: the communication-enabled interaction model.
Olger Siebinga is a PhD candidate at Delft University of Technology, working in the field of human-robot interaction. With a background in mechanical engineering, he has always been fascinated by the combination of mechanical, electrical, and software engineering. But throwing humans in the mix is what makes things really interesting. Many robots can behave safe and optimal in their own perfect, isolated world. But to make modern robots, such as automated vehicles, function in the real world, they must be able to interact with humans in a safe and natural manner. This is where his current work focusses on: understanding human (driving) behavior and describing it in a mathematical way such that automated vehicles can make decisions based on their understanding of humans.
@ASM social room, https://fau.zoom-x.de/j/65568671567?pwd=SCtVeUVEdllNSnBoYmNQcGl2MmZHUT09