Improving Efficiency of DNN-based Relocalization Module for Autonomous
  Driving with Server-side Computing
Dengbo Li Jieren Cheng Boyi Liu
In this work, we present a novel framework for camera relocation in
autonomous vehicles, leveraging deep neural networks (DNN). While existing
literature offers various DNN-based camera relocation methods, their deployment
is hindered by their high computational demands during inference. In contrast,
our approach addresses this challenge through edge cloud collaboration.
Specifically, we strategically offload certain modules of the neural network to
the server and evaluate the inference time of data frames under different
network segmentation schemes to guide our offloading decisions. Our findings
highlight the vital role of server-side offloading in DNN-based camera
relocation for autonomous vehicles, and we also discuss the results of data
fusion. Finally, we validate the effectiveness of our proposed framework
through experimental evaluation.