Jointly learnable behavior and trajectory planning for autonomous vehicles

A computer-implemented method, comprising:

• obtaining, from a plurality of sensors of an autonomous vehicle, situational data associated with an environment external to the autonomous vehicle;
• generating, by a machine-learned motion planning system using a unified cost function, a plurality of possible trajectories based at least in part on the situational data;
• and
• controlling a motion of the autonomous vehicle in the environment according to a trajectory of the plurality of possible trajectories selected by the machine-learned motion planning system and using the unified cost function.

One or more non-transitory computer-readable media that store instructions that are executable by one or more processors to cause a computing system to perform operations, the operations comprising:

• obtaining, from a plurality of sensors of the autonomous vehicle, situational data associated with an environment external to the autonomous vehicle;
• generating, by a machine-learned motion planning system and using a unified cost function, a plurality of possible trajectories based at least in part on the situational data;
• and
• controlling a motion of the autonomous vehicle in the environment according to a trajectory of the plurality of possible trajectories selected, by the machine-learned motion planning system and using the unified cost function.