RSS 2019 Workshop on Scene and Situation Understanding for Autonomous Driving (UAD2019)
June 22, 09:20 - 17:30
University of Freiburg, Technical Faculty
Georges-Köhler-Allee 101, Room 00 026
University of Freiburg, Technical Faculty
Georges-Köhler-Allee 101, Room 00 026
Enabling robust higher level scene and situation understanding is one of the key challenges to unlock the full potential of autonomous driving. Most autonomous driving research has considered the scientific problems involved in this challenge as a special instance of either the perception or the planning tasks. This workshop takes a scene and situation centric approach to discussing advances and future directions of autonomous driving research.
Our goal is to bridge the gap between perception, planning, and control-based approaches to scene and situation modeling. On the one hand, we want to discuss how higher-level scenery information can be used to improve the entire autonomy stack involving, localization, detection, planning, and control systems. On the other hand, we are interested in the interplay of classical perception, planning, and control approaches for obtaining an improved scenario understanding. In that context, we also discuss how recent advances in Deep and Reinforcement Learning can be leveraged for impacting basic research and actual deployment of autonomous vehicles.
Interested researchers from both, academia or industry are invited to submit extended abstracts to be presented in spotlight presentations and a poster session. The topics of interest involve but are not limited to:
We are proud to have a group of diverse invited speakers covering the entire spectrum of scene and and situation understanding research.
Vasileios Belagiannis
University of Ulm
Wolfram Burgard
Toyota Research Institute & University of Freiburg
Darius Burschka
TU Munich
Alexey Dosovitskiy
Google Brain
Dariu Gavrila
TU Delft
Yohannes Kassahun
Audi
Davide Scaramuzza
University of Zurich
Elena Stumm
Zoox