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.

我们提出了一个新的框架，用于自动驾驶汽车中的相机重定位，利用深度神经网络 (DNN)。通过边缘云协作，我们解决了现有方法在推理期间高计算需求的问题，通过将神经网络的某些模块策略性地卸载到服务器，并评估数据帧在不同网络分割方案下的推理时间，以指导我们的卸载决策。我们的研究结果突出了服务器端卸载在基于 DNN 的自动驾驶汽车相机重定位中的重要作用，我们还讨论了数据融合的结果。最后，通过实验评估验证了我们提出的框架的有效性。