US 12,288,402 B2
Automatically generating machine-learning training data
Robert Parenti, Dearborn, MI (US); Adil Nizam Siddiqui, Farmington Hills, MI (US); and Cynthia M. Neubecker, Westland, MI (US)
Assigned to Ford Global Technologies, LLC, Dearborn, MI (US)
Filed by Ford Global Technologies, LLC, Dearborn, MI (US)
Filed on Jun. 27, 2022, as Appl. No. 17/850,241.
Prior Publication US 2023/0419680 A1, Dec. 28, 2023
Int. Cl. G06V 20/58 (2022.01); B60W 40/08 (2012.01)
CPC G06V 20/58 (2022.01) [B60W 40/08 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01)] 16 Claims
OG exemplary drawing
 
1. 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, the nonenvironmental data including at least one of audio data or motion data, the motion data indicating motion of the first vehicle, the nonenvironmental data being time-synchronized with the first environmental data;
receiving speed data that is time-synchronized with the first environmental data;
determining a plurality of annotation times for a plurality of respective annotations based on the speed data, the annotations being derived from the nonenvironmental data at a plurality of respective record times, wherein determining the annotation times includes applying offsets to the record times, resulting in offsetted times, and applying the annotations at the offsetted times instead of the record times, the offsets dependent on the speed data;
adding the annotations derived from the at least one of the audio data or the motion data to the first environmental data, adding the annotations being based on the speed data, the speed data indicating a speed at which the first vehicle is traveling, wherein adding the annotations includes adding the annotations at the respective annotation times; 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.