Multi-Task Machine-Learned Models for Object Intention Determination in Autonomous Driving

A method for detecting and forecasting an actor in an environment of an autonomous vehicle, comprising:

• obtaining sensor data descriptive of the environment of the autonomous vehicle, the environment containing the actor;
• obtaining map data associated with the environment;
• and
• processing the sensor data and the map data with a machine-learned model comprising (i) one or more shared layers that are trained to generate intermediate features and (ii) one or more detection layers and one or more forecasting layers, wherein:
• the one or more detection layers process the intermediate features to generate a detection output descriptive of the actor in the environment, and
• the one or more forecasting layers process the intermediate features to generate:
• a first forecasting output descriptive of a trajectory indicative of an expected path of the actor towards a goal location, and
• a second forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions for the actor.

An autonomous vehicle (AV) control system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions for execution by the one or more processors that cause the AV control system to perform operations, the operations comprising:
• obtaining sensor data descriptive of an environment of the autonomous vehicle, the environment containing an actor;
• obtaining map data associated with the environment;
• and
• processing the sensor data and the map data with a machine-learned model comprising (i) one or more shared layers that are trained to generate intermediate features and (ii) one or more detection layers and one or more forecasting layers, wherein:
• the one or more detection layers process the intermediate features to generate a detection output descriptive of the actor in the environment, and
• the one or more forecasting layers process the intermediate features to generate:
• a first forecasting output descriptive of a trajectory indicative of an expected path of the actor towards a goal location, and
• a second forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions for the actor.

An autonomous vehicle, comprising:

• one or more sensors that generate sensor data descriptive of an environment of the autonomous vehicle;
• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions for execution by the one or more processors that cause the one or more processors to perform operations, the operations comprising:
• processing the sensor data and map data associated with the environment with a machine-learned model comprising (i) one or more shared layers that are trained to generate intermediate features and (ii) one or more detection layers and one or more forecasting layers, wherein:
• the one or more detection layers process the intermediate features to generate a detection output descriptive of an actor in the environment, and
• the one or more forecasting layers process the intermediate features to generate:
• a first forecasting output descriptive of a trajectory indicative of an expected path of the actor towards a goal location, and
• a second forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions for the actor.