About the Workshop
The CVPR 2021 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 extended submission deadline is March 26th 2021. Accepted papers will be presented at the poster session, some as orals and one paper will be awarded as the best paper.
We host three 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 Waymo, Argo AI and the Berkeley Deep Driving Consortium. Based on the datasets, we have define a multitude realistic problems and encourage new algorithms and pipelines to be invented for autonomous driving).
- Workshop paper submission deadline:
March 22nd 2021March 26th 2021Notification to authors: 16th April 2021Camera ready deadline: 19th April 2021
Topics 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 2021 paper templateThe page limit is 8 pages (excluding references)We accept dual submissions, 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/WAD2021
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 and Waymo. 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.
Details come soon!Datasets
Waymo Open Dataset
The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of conditions. We are releasing this dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology.
Argoverse by Argo AI
Argoverse is the first large-scale self-driving data collection to include HD maps with geometric and semantic metadata — such as lane centerlines, lane direction, and driveable area. All of the detail we provide makes it possible to develop more accurate perception algorithms, which in turn will enable self-driving vehicles to safely navigate complex city streets.
BDD100K Dataset from Berkeley DeepDrive
BDD100K dataset is a large collection of 100K driving videos with diverse scene types and weather conditions. Along with the video data, we also released annotation of different levels on 100K keyframes, including image tagging, object detection, instance segmentation, driving area and lane marking. In 2018, the challenges hosted at CVPR 2018 and AI Challenger 2018 based on BDD data attracted hundreds of teams to compete for best object recognition and segmentation algorithms for autonomous driving.Organizers