Automatically generating machine-learning training data
AssigneeFord Global Technologies, LLC
InventorsRobert Parenti, Adil Nizam Siddiqui, Cynthia M. Neubecker
Life of this patent
Estimating the term…
Abstract
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first environmental data recorded by an environmental sensor on board a vehicle, receive nonenvironmental data recorded on board the vehicle independently of the first environmental data, add a plurality of annotations derived from the nonenvironmental data to the environmental data, and train a machine-learning program to process second environmental data by using the first environmental data as training data and the annotations as ground truth for the first environmental data.
Claims20
A computer comprising a processor and a memory, the memory storing instructions executable by the processor to:
- receive second environmental data of an area outside a second vehicle;
- execute a machine-learning program with the second environmental data as input;
- and
- actuate a component of the second vehicle based on the execution of the machine-learning program;
- wherein the machine-learning program is trained by:
- receiving first environmental data of an area outside a first vehicle recorded by an environmental sensor on board the first vehicle;
- receiving nonenvironmental data recorded on board the first vehicle about an inside of the first vehicle;
- adding a plurality of annotations derived from the nonenvironmental data to the first environmental data;
- and
- training the machine-learning program to process the second environmental data by using the first environmental data as training data and the annotations as ground truth for the first environmental data.
A method comprising:
- receiving first environmental data of an area outside a first vehicle recorded by an environmental sensor on board the first vehicle;
- receiving nonenvironmental data recorded on board the first vehicle about an inside of the first vehicle;
- adding a plurality of annotations derived from the nonenvironmental data to the first environmental data;
- training a machine-learning program to process second environmental data by using the first environmental data as training data and the annotations as ground truth for the first environmental data;
- installing the machine-learning program on a vehicle computer of a second vehicle;
- and
- actuating a component of the second vehicle based on the machine-learning program.
Dependencies are inferred from the claim text.