Sponsored by



8:45 – 9:30 Daniel Cremers, (TU Munich)
9:30 – 10:15 Erik Rosen, (Zenuity AB)
10:15 – 11:30 Poster Session & coffee break
11:30 – 12:15 Uwe Franke, (Daimler AG)
12:15 – 14:00 Lunch Break
14:00 – 14:45 Raquel Urtasun (Uber and University of Toronto)
14:45 – 16:15 Poster Session (same as above)
16:15 – 17:00 Dan Levi, (General Motors)
17:00 – 17:05 Concluding remarks

Poster Session


Fusing Geometry and Appearance for Road Segmentation

Gong Cheng, Yiming Qian, James Elder

Distantly Supervised Road Segmentation

Satoshi Tsutsui, Tommi Kerola, Shunta Saito

Detecting Nonexistent Pedestrians

Jui-Ting Chien, Chia-Jung Chou, Ding-Jie Chen, Hwann-Tzong Chen

Improving a real-time object detector with compact temporal information

Martin Ahrnbom, Morten Jensen, Kalle Åström, Mikael Nilsson, Håkan Ardö, Thomas Moeslund

Real-time category-based and general obstacle detection for autonomous driving

Dan Levi, Noa Garnett, Shai Silberstein, Shaul Oron, Ethan Fetaya, Uri Verner, Ariel Ayash, Vlad Goldner, Rafi Cohen, Kobi Horn

Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior

Amir Rasouli, Iuliia Kotseruba, John Tsotsos

Going Deeper: Autonomous Steering with Neural Memory Networks

Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

Fast Vehicle Detector for Autonomous Driving

Che-Tsung Lin, Patrisia Santoso, Shu-Ping Chen, Hung-Jin Lin, Shang-Hong Lai

Large scale labelled video data augmentation for semantic segmentation in driving scenarios

Ignas Budvytis, Patrick Sauer, Thomas Roddick, Kesar Breen, Roberto Cipolla

Ladder-style DenseNets for Semantic Segmentation of Large Natural Images

Ivan Krešo, Sinisa Segvic, Josip Krapac

Risky Region Localization with Point Supervision

Kazuki Kozuka, Juan Carlos Niebles

HyKo: A Spectral Dataset for Scene Understanding

Christian Winkens, Florian Sattler, Veronika Adams, Dietrich Paulus

Eliminating the observer effect: Shadow removal in orthomosaics of the road network

Supannee Tanathong, William Smith, Stephen Remde

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 large 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 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 3 double blind reviews, which will be moderated by the workshop chairs.


Invited Speakers

Important Dates


Organizing Committee

Program Committee

Paper Submission

Papers should describe original and unpublished work about the above or closely related topics. Each paper will receive double blind reviews, moderated by the workshop chairs. Authors should take into account the following: