| Summary: | Abstract The 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.
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