UAD 2019

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

Workshop Description

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.

Sponsor

News

July 8, 2019 - All posters and extended abstracts are now available online.
June 22, 2019 - The Workshop was a great success. Thank you everyone for attending. Congratulations to Berta Bescos, Cesar Cadena, and Jose Neira for winning the TRI Best Contribution Award!
June 14, 2019 - The list of accepted posters is now available online.
June 13, 2019 - We are happy to announce that Toyota Research Institute will be sponsoring the Best Contribution Award of our Workshop.

Call for Contributions

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:

Situation-aware planning for autonomous vehicles.
Semantic scene understanding-based approaches to Localization and Mapping.
Traffic agent trajectory forecasting.
Intent prediction.
Multi-Agent reinforcement learning for situation aware planning of autonomous vehicles.
Novel sensing-modalities for improved scene and situation understanding.
Domain adaptation and transfer learning in the context of autonomous driving.