CPC G06F 9/4881 (2013.01) [B60K 31/00 (2013.01); B60K 31/0008 (2013.01); G06F 9/3851 (2013.01); G06F 9/48 (2013.01); G06F 9/4806 (2013.01); G06F 9/4843 (2013.01); G06F 9/50 (2013.01); G06F 9/5005 (2013.01); G06F 9/5027 (2013.01); G06F 9/5038 (2013.01); G06F 9/5044 (2013.01); G06F 9/5094 (2013.01); G06N 5/00 (2013.01); G06N 20/00 (2019.01)] | 20 Claims |
1. A method comprising, at each of a plurality of time steps:
receiving data that characterizes an environment in a vicinity of a vehicle at a current time step, the environment comprising a plurality of agents;
receiving data that identifies, as high-priority agents, a proper subset of the plurality of agents for which respective data characterizing the agents must be generated at the current time step;
identifying computing resources provided by data processing computer hardware on-board the vehicle that are available for executing a task on-board the vehicle, the task comprising generating the respective data characterizing the high-priority agents in the environment in the vicinity of the vehicle at the current time step;
processing the data that characterizes the environment using a complexity scoring machine learning model to determine one or more respective complexity scores for each of the high-priority agents, each respective complexity score characterizing an estimated amount of computing resources that is required for executing the task on-board the vehicle;
determining, based on the complexity scores, a schedule for the current time step that allocates the execution of the task across the available computing resources that are provided by the data processing computer hardware on-board the vehicle; and
allocating, in accordance with the schedule, the execution of the task across the available computing resources that are provided by the data processing computer hardware on-board the vehicle.
|