Trajectory Generation Using Road Network Model
AssigneeMotional AD LLC
InventorsRobert Beaudoin, Eric Wolff
Life of this patent
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Abstract
Enclosed are embodiments for trajectory generation using a road network model. In an embodiment, a method includes: obtaining, using at least one processor of a vehicle, a location of the vehicle; obtaining, using the at least one processor, sensor data collected at the location; obtaining, using the at least one processor, map data for the location; generating, using the one or more processors, at least on possible trajectory for at least one object at the location, wherein the possible trajectory is constrained in accordance with the map data; and predicting, using a machine learning model, a score for the at least one trajectory.
Claims20
A method comprising:
- obtaining, using at least one processor, scene data, the scene data including a current location of a vehicle, sensor data captured at the location and road map data for the location;
- generating, using the at least one processor, at least one possible trajectory template for at least one agent at the location based on the scene data, wherein the at least one possible trajectory template is constrained in accordance with the road map data;
- and
- predicting, using the at least one processor, a score for the at least one possible trajectory template using a machine learning model.
A system comprising:
- at least one processor;
- memory storing instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising:
- obtaining scene data, the scene data including a current location of a vehicle, sensor data captured at the location and road map data for the location;
- generating at least one possible trajectory template for at least one agent at the location based on the scene data, wherein the at least one possible trajectory template is constrained in accordance with the road map data;
- and
- predicting a score for the at least one possible trajectory template using a machine learning model.
Dependencies are inferred from the claim text.