US 11,941,518 B2
Cooperative learning neural networks and systems
Fa-Long Luo, San Jose, CA (US); Tamara Schmitz, Scotts Valley, CA (US); Jeremy Chritz, Seattle, WA (US); and Jaime Cummins, Bainbridge Island, WA (US)
Assigned to Micron Technology, Inc., Boise, ID (US)
Filed by MICRON TECHNOLOGY, INC., Boise, ID (US)
Filed on Aug. 28, 2018, as Appl. No. 16/114,923.
Application 16/114,923 is a continuation of application No. 15/693,142, filed on Aug. 31, 2017.
Prior Publication US 2019/0065951 A1, Feb. 28, 2019
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); H04W 4/02 (2018.01); H04W 4/38 (2018.01); H04W 4/40 (2018.01); H04W 4/90 (2018.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01); G06N 3/045 (2023.01); H04W 4/02 (2013.01); H04W 4/023 (2013.01); H04W 4/38 (2018.02); H04W 4/40 (2018.02); H04W 4/90 (2018.02)] 26 Claims
OG exemplary drawing
 
1. A vehicle comprising:
a first sensor configured to capture first vehicle data;
a transceiver configured to receive second vehicle data from a second sensor of a second vehicle in response to capture of the second vehicle data at the second sensor, where the second vehicle data is received at the transceiver while the second vehicle is in communication with the transceiver, the vehicle configured to identify the second vehicle;
a processor configured to:
use, in the vehicle, a cooperative learning neural network trained on the first vehicle data and the second vehicle data received from the second sensor of the second vehicle to detect a crash condition, wherein, responsive to identifying that the second vehicle is within a threshold distance of the vehicle, and responsive to the receiving of the second vehicle data, training the cooperative learning neural network at the vehicle using the first vehicle data and the second vehicle data occurs while the second vehicle is within the threshold distance of the vehicle, and training the cooperative learning neural network using the first vehicle data and the second vehicle data does not occur, otherwise; and
a controller configured to adjust an operation of the vehicle based on the detection of the crash condition by the cooperative learning neural network.