CVPR 2021

Workshop on Autonomous Driving

June 20, 2021

Virtual - Worldwide

About The Workshop

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.

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Participants
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Participate
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Paper Submission

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.

Challenge Track

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).

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Call for Papers

Important Dates

  • Workshop paper submission deadline: March 22nd 2021 March 26th 2021
  • Notification to authors: 16th April 2021
  • Camera ready deadline: 19th April 2021

Topics Covered

Topics of the papers include but are not limited to:

  • Autonomous navigation and exploration
  • Vision based advanced driving assistance systems, driver monitoring and advanced interfaces
  • Vision systems for unmanned aerial and underwater vehicles
  • Deep Learning, machine learning, and image analysis techniques in vehicle technology
  • Performance evaluation of vehicular applications
  • On-board calibration of acquisition systems (e.g., cameras, radars, lidars)
  • 3D reconstruction and understanding
  • Vision based localization (e.g., place recognition, visual odometry, SLAM)

Presentation Guidelines

All accepted papers will be presented as posters. The guidelines for the posters are the same as at the main conference.

Submission Guidelines

  • We solicit short papers on autonomous vehicle topics
  • Submitted manuscript should follow the CVPR 2021 paper template
  • The 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 journal
  • Submissions will be rejected without review if they:
    • contain more than 8 pages (excluding references)
    • violate the double-blind policy or violate the dual-submission policy
  • The accepted papers will be linked at the workshop webpage and also in the main conference proceedings if the authors agree
  • Papers 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
Challenges

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.

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Challenge 1: Waymo Open Dataset Challenges

The 2021 Waymo Open Dataset Challenges are live! We are inviting eligible researchers to participate in four challenges involving both the new motion dataset, and existing perception dataset, in our Waymo Open Dataset.

  • Motion prediction challenge: Given agents' tracks for the past 1 second on a corresponding map, predict the positions of up to 8 agents for 8 seconds into the future.
  • Interaction prediction challenge: Given agents' tracks for the past 1 second on a corresponding map, predict the joint future positions of 2 interacting agents for 8 seconds into the future.
  • Real-time 3D detection: Given three lidar range images and the associated camera images, produce a set of 3D upright boxes for the objects in the scene.
  • Real-time 2D detection: Given a set of camera images, produce a set of 2D boxes for the objects in the scene, with a latency requirement.

The winning team for each challenge will receive a $15,000 cash award, with second-place teams receiving $5,000 and third place $2,000.
You can find the rules for participating in the challenges here. The challenges close at 11:59pm Pacific on May 31, 2021, but the leaderboards will remain open for future submissions.

Challenge 2: Argoverse Challenges

Our second two challenges are the Argoverse Motion Forecasting and Stereo challenges. Argo AI is offering $8,000 in prizes for Motion Forecasting and Stereo competitions on Argoverse. See more details on the Motion Forecasting and Stereo leaderboards. The competition will end on June 13th. Winning methods will be highlighted during the workshop.

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Datasets
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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.

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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.