  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 $98\%$.
