  This letter proposes an edge learning-based offloading framework for
autonomous driving, where the deep learning tasks can be offloaded to the edge
server to improve the inference accuracy while meeting the latency constraint.
Since the delay and the inference accuracy are incurred by wireless
communications and computing, an optimization problem is formulated to maximize
the inference accuracy subject to the offloading probability, the pre-braking
probability, and data quality. Simulations demonstrate the superiority of the
proposed offloading framework.
