Welcome to the 1st Autonomous Vehicle Vision

The 1st Autonomous Vehicle Vision (AVVision) workshop aims to bring together industry professionals and academics to brainstorm and exchange ideas on the advancement of visual environment perception for autonomous driving. In this one-day workshop, we will have regular paper presentations and invited speakers to present the state of the art as well as the challenges in autonomous driving. Furthermore, we have prepared several large-scale, synthetic and real-world datasets, which have been annotated by CalmCar, HKUST, UDI, and ATG Robotics. Based on these datasets, three challenges will be hosted to understand the current status of computer vision and machine/deep learning algorithms in solving the visual environment perception problems for autonomous driving:

• CalmCar MTMC Tracking Challenge;
• HKUST-UDI UDA Challenge;
• KITTI Object Detection Challenge.

Keynote Talks

Towards Robust End-to-End Driving (Andreas Geiger)

Semantic 3D world modeling (Ioannis Pitas)

Object Detection and Motion Prediction for Safe Self-Driving using Raster-Based Methods (Nemanja Djuric)

Pixel Processor Arrays to Bridging Perception and Action in Agile Robots (Walterio Mayol-Cuevas)


01/10/2021: Thank you for attending the 1st AVVision workshop! If you would like to subscribe our mailing list for the future AVVision events, please send your email address to avvision@mias.group!
01/06/2021: the accepted papers are available at https://openaccess.thecvf.com/WACV2021_workshops/AVV!
01/06/2021: the regular paper presentations are available now!
01/05/2021: we will organize an AVVision Sepcial Session at ICIP'21!
12/23/2020: the workshop program is now available!
11/29/2020: eight regular papers are accepted to this workshop!
11/08/2020: the submission system will be temporarily closed between 11/09/2020 and 11/22/2020!
10/25/2020: the CalmCar MTMC Tracking Challenge instructions are released!
10/23/2020: the HKUST-UDI UDA Challenge instructions are released!
10/01/2020: the submission system is now open: https://cmt3.research.microsoft.com/AVV2021/!

Past Events