Connected vehicle network data transfer optimization

Patent metadata

Field	Value
Publication number	US-11917431-B2
Application number	US-202318179074-A
Country	US
Kind code	B2
Filing date	Mar 6, 2023
Priority date	Aug 8, 2018
Publication date	Feb 27, 2024
Grant date	Feb 27, 2024

Abstract

Generally described, one or more aspects of the present application correspond to techniques for dynamic management of the timing of data transfer between a connected vehicle and a remote computing system. For example, during navigation a connected vehicle may switch between connections to a number of different networks, each having different parameters (cost, bandwidth, quality, etc.). The disclosed techniques can use inputs including vehicle location, available networks, and data transfer timing requirements to optimize data transfer with respect to one or more of these parameters.

First claim

What is claimed is:

1. A networking system for a vehicle, the networking system comprising:
at least one transceiver configured to connect to a plurality of networks; and
one or more processors configured by computer-executable instructions to act as an intermediary between an application executing on a computing system of the vehicle and at least one server remote from the vehicle, by at least:
determining a data transfer window for transferring data between the application and the at least one server;
receiving a predetermined navigational route of the vehicle to a geographic destination;
determining, based on the predetermined navigational route of the vehicle, an expected location of the vehicle during the data transfer window;
identifying, based on the determined expected location, at least two networks to which the at least one transceiver can connect during the data transfer window;
selecting, from the at least two networks, a network or combination of networks corresponding to a lowest cost to transfer data between the application and the at least one server; and
causing the data to be transferred, via the at least one transceiver, between the application and the at least one server, using the selected network or combination of networks during the data transfer window.

2. The networking system of claim 1 , wherein the one or more processors are further configured to determine a start time within the data transfer window for transferring the data.

3. The system of claim 2 , wherein the start time is later than a beginning of the data transfer window.

4. The system of claim 2 , wherein the one or more processors are configured to determine the start time based at least in part on a deep learning model trained to predict optimal data transfer start and stop timings.

5. The system of claim 1 , wherein the one or more processors are configured to determine the expected location of the vehicle during the data transfer window based at least in part on historical navigation data associated with the vehicle.

6. The system of claim 1 , wherein the one or more processors are configured to determine the expected location of the vehicle during the data transfer window based at least in part on a current speed of the vehicle, a trending speed of the vehicle, one or more speed limits associated with roads along the predetermined navigational route of the vehicle, or one or more speed trends associated with roads along the predetermined navigational route of the vehicle.

7. The system of claim 1 , wherein the one or more processors are configured to determine the expected location of the vehicle during the data transfer window based in part on current or predicted traffic conditions associated with roads along the predetermined navigational route of the vehicle.

8. The system of claim 1 , wherein the selected network or combination of networks comprises a combination of at least two networks.

9. The system of claim 8 , wherein the combination of at least two networks includes a first network corresponding to a lowest cost and a second network corresponding to a next-lowest cost.

10. The system of claim 9 , wherein causing the data to be transferred comprises causing a first portion of the data to be transferred using the first network and causing a second portion of the data to be transferred using the second network during a portion of the data transfer window when the first network is unavailable.

11. A computer-implemented method comprising, by one or more processors of a vehicle:
determining a data transfer window for transferring data between an application executing on a computing system of the vehicle and at least one server remote from the vehicle;
receiving a predetermined navigational route of the vehicle to a geographic destination;
determining, based on the predetermined navigational route of the vehicle, an expected location of the vehicle during the data transfer window;
identifying, based on the determined expected location, at least two networks to which at least one transceiver of the vehicle can connect during the data transfer window;
selecting, from the at least two networks, a network or combination of networks corresponding to a lowest cost to transfer data between the application and the at least one server; and
causing the data to be transferred, via the at least one transceiver, between the application and the at least one server, using the selected network or combination of networks during the data transfer window.

12. The method of claim 11 , further comprising determining a start time within the data transfer window for transferring the data.

13. The method of claim 12 , wherein the start time is later than a beginning of the data transfer window.

14. The method of claim 12 , wherein the start time is determined based at least in part on a deep learning model trained to predict optimal data transfer start and stop timings.

15. The method of claim 11 , wherein the expected location of the vehicle during the data transfer window is determined based at least in part on historical navigation data associated with the vehicle.

16. The method of claim 11 , wherein the expected location of the vehicle during the data transfer window is determined based at least in part on a current speed of the vehicle, a trending speed of the vehicle, one or more speed limits associated with roads along the predetermined navigational route of the vehicle, or one or more speed trends associated with roads along the predetermined navigational route of the vehicle.

17. The method of claim 11 , wherein the expected location of the vehicle during the data transfer window is determined based at least in part on current or predicted traffic conditions associated with roads along the predetermined navigational route of the vehicle.

18. The method of claim 11 , wherein the selected network or combination of networks comprises a combination of at least two networks.

19. The method of claim 18 , wherein the combination of at least two networks includes a first network corresponding to a lowest cost and a second network corresponding to a next-lowest cost.

20. The method of claim 19 , wherein causing the data to be transferred comprises causing a first portion of the data to be transferred using the first network and causing a second portion of the data to be transferred using the second network during a portion of the data transfer window when the first network is unavailable.

Assignees

• Faraday&Future Inc

Inventors

• Sundar Shyam

Classifications

• G06N3/09

  Supervised learning · CPC title

• H04W24/02 Primary

  Arrangements for optimising operational condition · CPC title

• H04W4/44

  for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H] · CPC title

• H04W72/12

  Wireless traffic scheduling · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title