Systems and methods for determining the position of a wireless access device within a vehicle

1. A method comprising:

receiving, from a wireless access device, a beacon message at each of two or more antennas positioned within a vehicle, wherein a single transceiver processes the beacon message received at each of the two or more antennas for position determination of the wireless access device by a vehicle control unit (VCU);

determining, based at least in part on the beacon message received at each of the two or more antennas, a position of the wireless access device relative to the vehicle using a machine learning model analysis of measured radio signal strengths of the beacon message at each of the two or more antennas; and

configuring one or more systems of the vehicle based at least in part on the position of the wireless access device relative to the vehicle, and

wherein the method further comprises:

transmitting, from the vehicle to a remote server as training data, the determined position of the wireless access device relative to the vehicle with signal parameters associated with at least one beacon message received by the two or more antennas, wherein the remote server uses the training data received from the vehicle and training data received from other vehicles to refine the machine learning model;

receiving an updated machine learning model from the remote server; and

using the updated machine learning model to determine a new position of the wireless access device relative to the vehicle.

2. The method of claim 1 , wherein an interior of the vehicle comprises a met of zones, each zone in the set of zones defined by one or more pathways between the two or more antennas.

3. The method of claim 2 , wherein determining the position of the wireless access device relative to the vehicle further comprises:

measuring a radio signal strength of the beacon message at each of the two or more antennas;

determining relative radio signal strengths of the beacon message at each of the two or more antennas; and

determining a zone among the set of zones that the wireless access device is located within based at least in part on the relative radio signal strengths of the beacon message at each of the two or more antennas and a position of each of the two or more antennas.

4. The method of claim 1 , wherein determining the position of the wireless access device relative to the vehicle comprises:

measuring a radio signal strength of the beacon message at each of the two or more antennas; and

determining the position of the wireless access device relative to the vehicle using the machine learning model analysis of the measured radio signal strengths and a position of each of the two or more antennas in the vehicle.

5. The method of claim 1 , wherein determining the position of the wireless access device relative to the vehicle further comprises:

measuring a radio signal strength of the beacon message at each of the two or more antennas; and

determining the position of the wireless access device relative to the vehicle using a trilateration analysis of the measured radio signal strengths and a position of each of the two or more antennas in the vehicle.

6. The method of claim 1 , wherein the one or more systems of the vehicle include driving systems, entertainment systems, doors, windows and seats.

7. The method of claim 1 , wherein the machine learning model is a trained long short-term memory network.

8. A system comprising:

two or more antennas positioned within a vehicle, each of the two or more antennas configured to receive a beacon message from a wireless access device;

a transceiver communicably coupled with the two or more antennas, the transceiver configured to process the beacon message received at each of the two or more antennas for use in position determination of the wireless access device; and

a processor communicably coupled with the transceiver, the processor configured to:

determine, based at least in part on the beacon message received at each of the two or more antennas, a position of a wireless access device relative to the vehicle using a machine learning model analysis of measured radio signal strengths of the beacon message at each of the two or more antennas; and

configure one or more systems of the vehicle based at least in part on the position of the wireless access device relative to the vehicle, and

wherein the processor is further configured to:

transmit, from the vehicle to a remote server as training data, the determined position of the wireless access device relative to the vehicle with signal parameters associated with at least one beacon message received by the two or more antennas, wherein the remote server uses the training data received from the vehicle and training data received from other vehicles to refine the machine learning model;

receive an updated machine learning model from the remote server; and

use the updated machine learning model to determine a new position of the wireless access device relative to the vehicle.

9. The system of claim 8 , wherein an interior of the vehicle comprises a set of zones, each zone in the set of zones defined by one or more pathways between the two or more antennas.

10. The system of claim 9 , wherein to determine the position of the wireless access device relative to the vehicle, the processor is further configured to:

measure a radio signal strength of the beacon message at each of the two or more antennas;

determine relative radio signal strengths of the beacon message at each of the two or more antennas; and

determine a zone among the set of zones that the wireless access device is located within based at least in part on the relative radio signal strengths of the beacon message at each of the two or more antennas and a position of each of the two or more antennas.

11. The system of claim 8 , wherein to determine the position of the wireless access device relative to the vehicle, the processor is configured to:

measure a radio signal strength of the beacon message at each of the two or more antennas; and

determine the position of the wireless access device relative to the vehicle using the machine learning model analysis of the measured radio signal strengths and a position of each of the two or more antennas in the vehicle.

12. The system of claim 8 , wherein to determine the position of the wireless access device relative to the vehicle, the processor is further configured to:

measure a radio signal strength of the beacon message at each of the two or more antennas; and

determine the position of the wireless access device relative to the vehicle using a trilateration analysis of the measured radio signal strengths and a position of each of the plurality of beacon transceivers on the vehicle.

13. The system of claim 8 , wherein the one or more systems of the vehicle include driving systems, entertainment systems, doors, windows and seats.

14. The system of claim 8 , wherein the machine learning model is a trained long short-term memory network.

15. A non-transitory computer readable storage medium including instructions that, when executed by one or more processors causes the one or more processors to:

transmit, to a wireless access device, a signal causing the wireless access device to transmit a beacon message to each of two or more antennas positioned within a vehicle;

determine, using the one or more processors, a position of the wireless access device relative to the vehicle based at least in part on the beacon message received at each of the two or more antennas, wherein the beacon message received at each of the two or more antennas is processed for use by the one or more processors by a single transceiver using a machine learning model analysis of measured radio signal strengths of the beacon message at each of the two or more antennas; and

configure one or more systems of the vehicle based at least in part on the position of the wireless access device relative to the vehicle, and

wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

transmit, from the vehicle to a remote server as training data, the determined position of the wireless access device relative to the vehicle with signal parameters associated with at least one beacon message received by the two or more antennas, wherein the remote server uses the training data received from the vehicle and training data received from other vehicles to refine the machine learning model;

receive an updated machine learning model from the remote server; and

use the updated machine learning model to determine a new position of the wireless access device relative to the vehicle.

16. The non-transitory computer readable storage medium of claim 15 , wherein to determine the position of the wireless access device relative to the vehicle, the processor is to:

measure a radio signal strength of the beacon message at each of the two or more antennas; and

determine the position of the wireless access device relative to the vehicle using the machine learning model analysis of the measured radio signal strengths and a position of each of the two or more antennas in the vehicle.

17. The non-transitory computer readable storage medium of claim 15 , wherein to determine the position of the wireless access device relative to the vehicle, the processor is to further:

measure a radio signal strength of the beacon message at each of the two or more antennas; and

determine the position of the wireless access device relative to the vehicle using a trilateration analysis of the measured radio signal strengths and a position of each of the two or more antennas in the vehicle.

18. The non-transitory computer readable storage medium of claim 15 , wherein the one or more systems of the vehicle include driving systems, entertainment systems, doors, windows and seats.