About the Workshop
The CVPR 2020 Workshop on Autonomous Driving (WAD) aims to gather researchers and engineers from academia and industry to discuss the latest advances in perception for autonomous driving. In this one-day workshop, we will have regular paper presentations, invited speakers, and technical benchmark challenges to present the current state of the art, as well as the limitations and future directions for computer vision in autonomous driving, arguably the most promising application of computer vision and AI in general. The previous chapters of the workshop at CVPR attracted hundreds of researchers to attend. This year, multiple industry sponsors also join our organizing efforts to push its success to a new level.
We solicit paper submissions on novel methods and application scenarios of CV for Autonomous vehicles. We accept papers on a variety of topics, including autonomous navigation and exploration, ADAS, UAV, deep learning, calibration, SLAM, etc.. Papers will be peer reviewed under double-blind policy and the submission deadline is 20th March 2020. Accepted papers will be presented at the poster session, some as orals and one paper will be awarded as the best paper.
We host a challenge to understand the current status of computer vision algorithms in solving the environmental perception problems for autonomous driving. We have prepared a number of large scale datasets with fine annotation, collected and annotated by Berkeley Deep Driving Consortium and others. Based on the datasets, we have define a set of four realistic problems and encourage new algorithms and pipelines to be invented for autonomous driving).
Raquel UrtasunUofT and Uber
Trevor DarrellUC Berkeley
Andreas GeigerMPI / University of Tübingen
Byron BootsUniversity of Washington
Andreas WendelKodiak Robotics
Emilio FrazzoliETH Zürich/nuTonomy
Dengxin DaiETH Zürich
- Workshop paper submission deadline: March 20th 2020Notification to authors: 9th April 2020Camera ready deadline: 16th April 2020
Topics CoveredTopics of the papers include but are not limited to:
- Autonomous navigation and explorationVision based advanced driving assistance systems, driver monitoring and advanced interfacesVision systems for unmanned aerial and underwater vehiclesDeep Learning, machine learning, and image analysis techniques in vehicle technologyPerformance evaluation of vehicular applicationsOn-board calibration of acquisition systems (e.g., cameras, radars, lidars)3D reconstruction and understandingVision based localization (e.g., place recognition, visual odometry, SLAM)
Presentation GuidelinesAll accepted papers will be presented as posters. The guidelines for the posters are the same as at the main conference.
- We solicit short papers on autonomous vehicle topicsSubmitted manuscript should follow the CVPR 2019 paper templateThe page limit is 8 pages (excluding references)We accept dual submissions to CVPR 2020 and WAD 2020, but the manuscript must contain substantial original contents not submitted to any other conference, workshop or journalSubmissions will be rejected without review if they:
- contain more than 8 pages (excluding references)violate the double-blind policy or violate the dual-submission policyThe accepted papers will be linked at the workshop webpage and also in the main conference proceedings if the authors agreePapers will be peer reviewed under double-blind policy, and must be submitted online through the CMT submission system at: https://cmt3.research.microsoft.com/WAD2020
We host challenges to understand the current status of computer vision algorithms in solving the environmental perception problems for autonomous driving. We have prepared a number of large scale datasets with fine annotation, collected and annotated by Berkeley DeepDriving, Argo AI. Based on the datasets, we have defined a set of several realistic problems and encourage new algorithms and pipelines to be invented for autonomous driving.Organizers