5. The computing system of claim 1 , wherein: to obtain the first feature data descriptive of the first object perceived by the autonomous vehicle the one or more processors are configured to determine gap data descriptive of one or more gaps associated with one or more additional vehicles perceived by the autonomous vehicle; and the first yield decision output by the machine-learned yield model instructs the autonomous vehicle to proceed through a particular gap of the one or more gaps or to come to a stop.
5. The computing system of claim 1 , wherein: to obtain the first feature data descriptive of the first object perceived by the autonomous vehicle the one or more processors are configured to determine gap data descriptive of one or more gaps associated with one or more additional vehicles perceived by the autonomous vehicle; and the first yield decision output by the machine-learned yield model instructs the autonomous vehicle to proceed through a particular gap of the one or more gaps or to come to a stop. 8. The computing system of claim 5 , wherein: to determine gap data descriptive of the one or more gaps associated with the one or more additional vehicles the one or more processors are configured to determine a plurality of gaps associated with the one or more additional vehicles; to receive the first yield decision as the output of the machine-learned yield model the one or more processors are configured to: obtain, through implementation of the machine-learned yield model, a plurality of scores respectively for the plurality of gaps; select the particular gap of the plurality of gaps based at least in part on the plurality of scores; and the one or more processors are further configured to generate a gap penalty for the particular gap of the plurality of gaps, wherein the gap penalty is used to guide motion of the autonomous vehicle through the particular gap.