Intelligent climate control in vehicles

1. A vehicle, comprising:

a climate control system; and

an artificial neural network configured to receive input parameters relevant to climate control in the vehicle and generate, based on the input parameters as a function of time, predictions of setting adjustments for the climate control system, wherein the input parameters include at least one setting of the climate control system and at least one operating parameter of the vehicle;

wherein the vehicle is configured to effectuate an adjustment of the climate control system or prompt an approval of the adjustment based at least in part on the predictions generated by the artificial neural network and in response to a determination that a probability of the approval of the adjustment is above a threshold.

2. The vehicle of claim 1 , wherein the climate control system includes an air conditioning system, or a heating system, or any combination thereof.

3. The vehicle of claim 2 , wherein the at least one operating parameter of the vehicle includes a speed of the vehicle traveling on a roadway.

4. The vehicle of claim 3 , wherein the climate control system includes a fan configured to circulate air into a cabin for one or more occupants of the vehicle; and the setting of the climate control system includes a speed of the fan.

5. The vehicle of claim 3 , wherein the setting of the climate control system includes a preferred temperature in the cabin.

6. The vehicle of claim 5 , wherein the at least one operating parameter of the vehicle further includes a current temperature in the cabin, a current temperature outside of the vehicle, a location of the vehicle, or any combination thereof.

7. The vehicle of claim 5 , wherein the setting of the climate control system includes a setting of a heated seat.

8. The vehicle of claim 7 , wherein the input parameters include a window opening setting, a setting of a moonroof or sunroof of the vehicle, or a setting of a convertible top of the vehicle, or any combination thereof.

9. The vehicle of claim 8 , wherein the vehicle is configured to effectuate a first adjustment predicted by the artificial neural network without requesting confirmation from an occupant in the vehicle.

10. The vehicle of claim 9 , wherein the vehicle is configured to request a confirmation from the occupant before effectuating a second adjustment predicted by the artificial neural network.

11. The vehicle of claim 1 , wherein the artificial neural network is further configured to predict a likelihood of the adjustment being accepted by one or more occupants in the vehicle; and the vehicle is configured to effectuate the adjustment in response to a determination that the likelihood is above a threshold.

12. The vehicle of claim 1 , wherein the artificial neural network is further configured to predict a likelihood of the adjustment being accepted by one or more occupants in the vehicle; and the vehicle is configured to request an approval from the one or more occupants in the vehicle to effectuate the adjustment in response to a determination that the likelihood is above a threshold.

13. The vehicle of claim 1 , further configured to train the artificial neural network to predict the adjustment based on adjustments initiated by one or more occupants in the vehicle using one or more control elements of the vehicle.

14. A method, comprising:

receiving, in an artificial neural network, input parameters relevant to cabin climate control in a vehicle;

generating, by the artificial neural network based on the input parameters as a function of time, predictions of setting adjustments for a climate control system; and

effectuating, by the vehicle, an adjustment of the climate control system or prompt an approval of the adjustment based at least in part on the predictions generated by the artificial neural network and in response to a determination that a probability of the approval of adjustment is above a threshold.

15. The method of claim 14 , wherein the input parameters include at least one setting of the climate control system and at least one operating parameter of the vehicle.

16. The method of claim 15 , wherein the artificial neural network includes a spiking neural network; and the method further includes:

training, in the vehicle, the artificial neural network to predict no adjustments in absence of corresponding adjustments in the input parameters initiated by one or more occupants in the vehicle.

17. The method of claim 15 , wherein the artificial neural network includes a spiking neural network; and the method further includes:

in response to first adjustments in the input parameters initiated by one or more occupants in the vehicle, training, in the vehicle, the artificial neural network to predict the first adjustments based on the input parameters.

18. The method of claim 15 , further comprising:

generating, by the artificial neural network based on the input parameters as a function of time, the probability of the adjustment being approved by at least one occupant in the vehicle.

19. A non-transitory computer storage medium storing instruction, which when executed on a computer system, cause the computer system to perform a method, the method comprising:

receiving, in an artificial neural network, input parameters relevant to cabin climate control in a vehicle;

generating, by the artificial neural network based on the input parameters as a function of time, a prediction of a setting adjustment for a climate control system;

generating, by the artificial neural network based on the input parameters as a function of time, a probability of the setting adjustment being approved by at least one occupant in the vehicle;

determining whether the probability is above a threshold;

instructing the vehicle to effectuate the setting adjustment or prompt an occupant in the vehicle to approve the setting adjustment, in response to a determination that the probability is above the threshold.

20. The non-transitory computer storage medium of claim 19 , wherein the method further comprises:

detecting a response from the occupant responsive to the instructing the vehicle to effectuate the setting adjustment or prompt the occupant to approve the setting;

computing an adjusted probability of the setting adjustment based on the response; and

training the artificial neural network to predict the adjusted probability based on the input parameters as a function of time.