Author:
Li Dengbo,Zhang Hanning,Cheng Jieren,Liu Bernie
Abstract
AbstractThe substantial computational demands associated with Deep Neural Network (DNN)-based camera relocalization during the reasoning process impede their integration into autonomous vehicles. Cost and energy efficiency considerations may dissuade automotive manufacturers from employing high-computing equipment, limiting the adoption of advanced models. In response to this challenge, we present an innovative edge cloud collaborative framework designed for camera relocalization in autonomous vehicles. 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.
Funder
National Natural Science Foundation of China
the Key Research and Development Program of Hainan Province
the Major science and technology project of Hainan Province
Hainan Provincial Natural Science Foundation of China
Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software