High-Scalable Real-Time Cooperative Perception
towards Safe Autonomous Driving
Presentation Abstract
Cooperative perception is the key approach to augment the perception of ego vehicle toward safe autonomous driving. However, it is challenging to achieve real-time perception sharing for hundreds of vehicles in large-scale deployment scenarios. In this talk, we introduce AdaMap, a new high-scalable
real-time cooperative perception system, which achieves guaranteed end-to-end latency under time varying network dynamics. To achieve AdaMap, we design a tightly coupled data plane and control plane. In the data plane, we design a new hybrid localization module to dynamically switch between object detection and tracking, and a novel encoder-decoder representation module to adaptively
compress and recover the point cloud of detected objects. In the control plane, we design a new graph based object selection method to un-select excessive multi-viewed point clouds of objects, and a novel approximated gradient descent algorithm to optimize the representation factor of point clouds.
About the Speaker
Qiang Liu is an Assistant Professor at the School of Computing, University of
Nebraska-Lincoln. He earned his Ph.D. degree in Electrical Engineering from the
University of North Carolina at Charlotte (UNCC) in 2020, and received the
Outstanding Graduate Student Award from the Department of Electrical and
Computer Engineering at UNCC in 2019. His papers won IEEE Communications
Society’s Transmission, Access, and Optical Systems (TAOS) Best Paper Award
2019, and IEEE International Conference on Communications (ICC) Best
Paper Award 2019 and 2022. His research interests lie in the broad field of
edge computing, wireless communication, computer networking, and machine
learning.
Join us in person:
Friday, September 15, 2023
11:00 - 11:50 AM Central Time
Nebraska Hall (NH) Room 404 (in person), Lincoln
Peter Kiewit Institute (PKI) Room 160 (remote), Omaha