US 12,459,359 B2
Situational recommendations and control
Ramesh Sethu, Troy, MI (US); Arun Adiththan, Sterling Heights, MI (US); Md Mhafuzul Islam, Warren, MI (US); Prakash M. Peranandam, Rochester Hills, MI (US); Amirhossein Zolfagharian, Ottawa (CA); and Zohreh Aghababaeyan, Ottawa (CA)
Assigned to GM Global Technology Operations LLC, Detroit, MI (US); and University of Ottawa, Ottawa (CA)
Filed by GM Global Technology Operations LLC, Detroit, MI (US); and University of Ottawa, Ottawa (CA)
Filed on Sep. 5, 2023, as Appl. No. 18/460,952.
Prior Publication US 2025/0074443 A1, Mar. 6, 2025
Int. Cl. B60K 35/26 (2024.01); B60K 35/10 (2024.01); B60K 35/28 (2024.01); B60W 50/00 (2006.01); B60W 50/14 (2020.01)
CPC B60K 35/265 (2024.01) [B60K 35/28 (2024.01); B60W 50/0097 (2013.01); B60W 50/14 (2013.01); B60K 35/10 (2024.01); B60K 2360/119 (2024.01); B60K 2360/1438 (2024.01); B60K 2360/1468 (2024.01); B60W 2050/0029 (2013.01); B60W 2050/143 (2013.01); B60W 2050/146 (2013.01); B60W 2420/54 (2013.01); B60W 2540/21 (2020.02); B60W 2540/215 (2020.02); B60W 2556/10 (2020.02); B60W 2556/65 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method of providing situational recommendations within a vehicle, comprising:
collecting, with a plurality of onboard sensors in communication with a system controller, real-time data related to a location of the vehicle and operating conditions of the vehicle;
accessing, with the system controller, a database of stored data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred;
predicting, with a driver specific machine learning model within the system controller, a desired action based on the real-time data related to the location of the vehicle and the operating conditions of the vehicle and data from the database, including at least one of:
identifying a pattern of behavior indicating that the occupant takes a specific action each time the occupant arrives at a specific location; and
identifying a pattern of behavior indicating that the occupant takes a specific action each time a specific condition exists;
probabilistically predicting, with the machine learning model, the desired action based on the identified pattern of behavior;
initiating the predicted desired action, including:
when a probability of the desired action exceeds fifty percent, prompting the occupant within the vehicle with a recommendation for the predicted desired action; and
when the probability of the desired action exceeds ninety percent, automatically initiating the predicted desired action;
receiving input from an occupant within the vehicle;
updating the driver specific machine learning model;
receiving, via communication between the system controller and onboard systems within the vehicle, data related to an action being taken by the occupant within the vehicle;
comparing the action being taken by the occupant within the vehicle to the predicted desired action, and at least one of:
when the action being taken by the occupant does not match the predicted desired action, prompting the occupant within the vehicle to verify that the occupant within the vehicle wants to proceed with the action;
when the action being taken by the occupant is identified, by the machine learning model, as an anomaly, prompting the occupant within the vehicle to verify that the occupant within the vehicle wants to proceed with the action; and
when the action being taken by the occupant is identified by the system controller as an inherently unsafe action, prompt the occupant within the vehicle with a warning message.