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.

翻译：本文提出了一种利用深度神经网络(DNN)进行自动驾驶车辆相机重定位的新框架。现有文献提供了多种基于DNN的相机重定位方法，但这些方法在实际部署中受到推理阶段高计算需求的制约。相比之下，我们的方法通过边缘云协作来解决这一挑战。具体而言，我们策略性地将神经网络的某些模块卸载到服务器端，并在不同网络分割方案下评估数据帧的推理时间，以指导我们的卸载决策。研究结果凸显了服务器端卸载在基于DNN的自动驾驶车辆相机重定位中的关键作用，同时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框架的有效性。