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Program

8:25 Welcome Remarks
8:30-9:10 Mohan Trivedi (UCSD, USA)
Machine Vision for Human-Centered Autonomous Driving
9:10-9:50 Henning Hamer (Continental, Germany)
Continental's eHorizon and Road Database
9:50-11:50 Coffee break & Poster Session
11:50-12:30 Dariu Gavrila (TU Delft, Netherlands)
EuroCity Persons: A Novel Benchmark for Vulnerable Road User Detection
   
12:30 Lunch
   
14:00-14:40 Arnaud de la Fortelle (Mines ParisTech, France)
The perception-decision gap
14:40-15:20 Oscar Beijbom (nuTonomy, USA)
The Deep Learning Toolchain for Autonomous Driving
15:20 Closing remarks
List of accepted papers
Semantic Segmentation of Fisheye Images
Gregor Blott (Robert Bosch); Masato Takami (Robert Bosch); Christian Heipke (Universitat Hannover)
Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds
Martin Simon (Valeo); Stefan Milz (Valeo); Karl Amende (Valeo); Horst-Michael Grob (Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab)
Real-time point cloud alignment for vehicle localization in a high resolution 3D map
Balazs Nagy (MTA SZTAKI); Csaba Benedek (MTA SZTAKI)
Exploiting single image depth prediction for mono-stixel estimation
Fabian Brickwedde (Robert Bosch GmbH); Steffen Abraham (Robert Bosch GmbH); Rudolf Mester (Goethe University Frankfurt)
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Mohsen Ghafoorian (TomTom); Cedric Nugteren (TomTom); Nora Baka (TomTom); Olaf Booij (TomTom); Michael Hofmann (TomTom)
It's Not All About Size: On the Role of Data Properties in Pedestrian Detection
Amir Rasouli (York University); Iuliia Kotseruba (York University); John Tsotsos (York University)
Scale Drift Correction of Camera Geo-Localization using Geo-Tagged Images
Kazuya Iwami (The University of Tokyo); Satoshi Ikehata (National Institute of Informatics); Kiyoharu Aizawa (The University of Tokyo)
Distant Vehicle Detection: How Well Can Region Proposal Networks Cope With Tiny Objects at Low Resolution?
Ann-Katrin Fattal (Continental AG and TU Darmstadt); Michelle Karg (Continental AG); Christian Scharfenberger (Continental AG)

Invited Speakers

Topics of Interest

Analyzing road scenes using cameras could have a crucial impact in many domains, such as autonomous driving, advanced driver assistance systems (ADAS), personal navigation, mapping of large scale environments and road maintenance. For instance, vehicle infrastructure, signage, and rules of the road have been designed to be interpreted fully by visual inspection. As the field of computer vision becomes increasingly mature, practical solutions to many of these tasks are now within reach. Nonetheless, there still seems to exist a wide gap between what is needed by the automotive industry and what is currently possible using computer vision techniques.

 

The goal of this workshop is to allow researchers in the fields of road scene understanding and autonomous driving to present their progress and discuss novel ideas that will shape the future of this area. In particular, we would like this workshop to bridge the gap between the community that develops novel theoretical approaches for road scene understanding and the community that builds working real-life systems performing in real-world conditions. To this end, we will aim to have invited speakers covering different continents and coming from both academia and industry.

 

We encourage submissions of original and unpublished work in the area of vision-based road scene understanding. The topics of interest include (but are not limited to):

 

We encourage researchers to submit not only theoretical contributions, but also work more focused on applications. Each paper will receive double blind reviews, which will be moderated by the workshop chairs.

Important Dates

Organizing Committee

Program Committee