Method and system to predict one or more trajectories of a vehicle based on context surrounding the vehicle

A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for operating an autonomous vehicle, the operations comprising:

• perceiving a surrounding environment of the autonomous vehicle to identify one or more vehicles nearby;
• for each of the identified vehicles,
• based on a current location of the identified vehicle, obtaining vehicle-independent information, including physical constraints imposed on the identified vehicle,
• predicting a first set of one or more trajectories for the identified vehicle by applying a first machine learning model to a first set of features extracted from the vehicle-independent information associated with the identified vehicle,
• predicting a second set of one or more trajectories by applying a second machine learning model to a second set of features extracted from sensor data captured by one or more sensors of the autonomous vehicle perceiving behaviors of the identified vehicle, and
• determining a final set of predicted trajectories based on the first set of trajectories and the second set of trajectories;
• and
• controlling the autonomous vehicle based on one or more final predicted trajectories of the one or more identified vehicles.

A data processing system, comprising:

• a processor;
• and
• a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for operating an autonomous vehicle, the operations including:
• perceiving a surrounding environment of the autonomous vehicle to identify one or more vehicles nearby;
• for each of the identified vehicles,
• based on a current location of the identified vehicle, obtaining vehicle-independent information, including physical constraints imposed on the identified vehicle,
• predicting a first of one or more trajectories for the identified vehicle by applying a first machine learning model to a first set of features extracted from the vehicle-independent information associated with the identified vehicle,
• predicting a second set of one or more trajectories by applying a second machine learning model to a second set of features extracted from sensor data captured by one or more sensors of the autonomous vehicle perceiving behaviors of the identified vehicle, and
• determining a final set of predicted trajectories based on the first set of trajectories and the second set of trajectories;
• and
• controlling the autonomous vehicle based on one or more final predicted trajectories of the one or more identified vehicles.