Edge intelligence autonomous driving (EIAD) offers computing resources in autonomous vehicles for training deep neural networks. However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles. Moreover, the required number of training samples for different data modalities, e.g., images, point-clouds, is diverse. Consequently, when collecting these datasets from vehicles to the edge server, the associated bandwidth and power allocation across all data frames is a large-scale multi-modal optimization problem. This article proposes a highly computationally efficient algorithm that directly maximizes the quality of training (QoT). The key ingredients include a data-driven model for quantifying the priority of data modality and two first-order methods termed accelerated gradient projection and dual decomposition for low-complexity resource allocation. Finally, high-fidelity simulations in Car Learning to Act (CARLA) show that the proposed algorithm reduces the perception error by 3% and the computation time by 98%.
@article{arxiv.2210.09659,
title = {Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving},
author = {Xinrao Li and Tong Zhang and Shuai Wang and Guangxu Zhu and Rui Wang and Tsung-Hui Chang},
journal= {arXiv preprint arXiv:2210.09659},
year = {2022}
}