  Autonomous vehicles usually consume a large amount of computational power for
their operations, especially for the tasks of sensing and perception with
artificial intelligence algorithms. Such a computation may not only cost a
significant amount of energy but also cause performance issues when the
computational resources are limited. To address this issue, this paper proposes
an adaptive optimization method to online allocate the onboard computational
resources of an autonomous vehicle amongst multiple vehicular subsystems
depending on the contexts of the situations that the vehicle is facing.
Different autonomous driving scenarios were designed to validate the proposed
approach and the results showed that it could help improve the overall
performance and energy consumption of autonomous vehicles compared to existing
computational arrangement.
