US 11,887,409 B2
Device health code broadcasting on mixed vehicle communication networks
Kai Chen, San Jose, CA (US); and Pingfan Meng, San Bruno, CA (US)
Assigned to Pony Al Inc., Grand Cayman (KY)
Filed by Pony Al Inc., Grand Cayman (KY)
Filed on May 19, 2021, as Appl. No. 17/324,924.
Prior Publication US 2022/0375274 A1, Nov. 24, 2022
Int. Cl. G07C 5/00 (2006.01); G07C 5/08 (2006.01); B60W 50/02 (2012.01); B60W 30/08 (2012.01)
CPC G07C 5/008 (2013.01) [B60W 30/08 (2013.01); B60W 50/0205 (2013.01); G07C 5/0816 (2013.01); B60W 2050/021 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving, by one or more processors, a first device health code broadcasted over a first vehicle communication network, wherein the first device health code is generated by a first vehicle component that experienced a first fault condition;
receiving, by one or more of the processors, the first device health code broadcasted over a second vehicle communication network, wherein the second vehicle communication network utilizes a different communication protocol than the first vehicle communication network;
parsing, by one or more of the processors, the first device health code to identify first fault information contained in the first device health code;
retrieving, by one or more of the processors, current vehicle operational data;
predicting, by one or more processors, in association with a machine learning algorithm, technique, or model, a vehicle response measure to the first fault condition based at least in part on the first fault information and the current vehicle operational data;
generating, by one or more of the processors, one or more control commands indicative of the vehicle response measure; and
sending, by one or more of the processors, the one or more control commands to a vehicle actuation system to effectuate the vehicle response measure;
receiving feedback, by one or more of the processors, regarding an efficacy of the vehicle response measure; and
updating, by one or more of the processors, the machine learning algorithm, technique, or model based on the received feedback.