Systems and Methods for Selecting Trajectories Based on Interpretable Semantic Representations

A computing system for an autonomous vehicle, the system comprising:

• one or more processors and one or more non-transitory computer-readable memories;
• wherein the one or more non-transitory computer-readable memories store instructions that, when executed by the processor, cause the computing system to perform operations, the operations comprising:
• obtaining map data for a geographic area and sensor data obtained by the autonomous vehicle;
• identifying, using one or more machine-learned models, feature data included in the map data and sensor data;
• for a respective semantic object type from a plurality of semantic object types, determining, using feature data as input to a machine-learned model associated with the respective semantic object type, one or more occupancy maps for one or more timesteps in the future;
• and
• selecting a trajectory for the autonomous vehicle based on a plurality of occupancy maps associated with the plurality of semantic object types.

An autonomous vehicle, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• obtaining map data for a geographic area and sensor data obtained by the autonomous vehicle;
• identifying, using one or more machine-learned models, feature data included in the map data and sensor data;
• for a respective semantic object type from a plurality of semantic object types, determining, using feature data as input to a machine-learned model associated with the respective semantic object type, one or more occupancy maps for one or more timesteps in the future;
• and
• selecting a trajectory for the autonomous vehicle based on a plurality of occupancy maps associated with the plurality of semantic object types.