Optimizing communication for automated vehicles

• US 9,603,158 B1
• Filed: 12/08/2015
• Issued: 03/21/2017
• Est. Priority Date: 12/08/2015
• Status: Active Grant

• Current Assignee: Aurora Operations Incorporated (Aurora Innovation, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Ross, William, Aitken, Michael
• Primary Examiner(s): Wong, Warner
• Application Number:
  US14/962,847
• Time in Patent Office: 469 Days
• Field of Search: 370/330, 370/232
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: H04L 67/04 specially adapted for terminals or networks with limited capabilities; specially adapted for terminal portability
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70
• CPC Class Codes: H04L 67/61 taking into account QoS or priority requirements
• CPC Class Codes: H04L 69/26 Special purpose or proprietary protocols or architectures network applications for proprietary or special purpose networking environments H04L67/12
• CPC Class Codes: H04W 16/18 Network planning tools
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 36/1446 wherein at least one of the networks is unlicensed
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• CPC Class Codes: H04W 48/16 Discovering, processing access restriction or access information
• CPC Class Codes: H04W 72/542 using measured or perceived quality

Abstract

A backend system for a fleet of autonomous vehicles (AVs) for a given region can store a spectrum heat map indicating network coverage strength for a plurality of network types sourced at base stations located throughout the given region. The backend system can dynamically receive network quality data from the plurality of AVs traveling throughout the given region, and dynamically update the spectrum heat map based on the received network quality data.

Claims

1. A backend system for communicating with a plurality of automated vehicles (AVs) within a given region, the backend system comprising:

one or more communication interfaces;

one or more processors; and

one or more memory resources storing instructions that, when executed by the one or more processors, cause the backend system to;

store a spectrum heat map in the one or more memory resources, the spectrum heat map indicating network coverage strength for a plurality of network types sourced at base stations located throughout the given region;

dynamically receive, via the one or more communication interfaces, network quality data for each of the plurality of network types from the plurality of AVs traveling throughout the given region;

dynamically update the spectrum heat map based on the received network quality data;

identify a travel route for a selected one of the plurality of AVs;

using the updated spectrum heat map, identify a plurality of the base stations and a corresponding plurality of network types with coverage along the travel route;

determine an optimal connection schedule for the selected AV, the optimal connection schedule indicating location points along the travel route at which the selected AV is to switch from a previous network connection to a succeeding network connection; and

transmit the optimal connection schedule to the selected AV to enable the selected AV to switch connections at the location points along the travel route.

2. The backend system of claim 1, wherein the plurality of network types indicated on the spectrum heat map comprises one or more of a 3G, a 4G, a long-term evolution (LTE), or a WiFi network type.

3. The backend system of claim 2, wherein the plurality of network types indicated on the spectrum heat map further comprises one or more of a WiMax, a WiGig, or a dedicated short range communications (DSRC) network type.

4. The backend system of claim 3, wherein the plurality of network types indicated on the spectrum heat map further comprises a 900 MHz band network type.

5. The backend system of claim 1, wherein the executed instructions further cause the backend system to:

receive a pick-up request from a requesting user in the given region;

in response to receiving the pick-up request, identify that the selected AV is proximate to a pick-up location identified in the pick-up request; and

instruct the selected AV to travel to the pick-up location to service the pick-up request.

6. The backend system of claim 5, wherein the pick-up request further includes a destination location, and wherein the travel route comprises a route from the pick-up location to the destination location.

7. The backend system of claim 1, wherein the executed instructions cause the backend system to determine the optimal connection schedule by identifying, from the corresponding plurality of network types indicated on the spectrum heat map, a string of networks along the travel route that have a highest respective bandwidth.

8. A computer-implemented method for managing transportation for a plurality of automated vehicles (AVs) within a given region, the method performed by one or more processors of a backend system in communication with the plurality of AVs and comprising:

storing a spectrum heat map in a memory resource, the spectrum heat map indicating network coverage strength for a plurality of network types sourced at base stations located throughout the given region;

dynamically receiving network quality data for each of the plurality of network types from the plurality of AVs traveling throughout the given region;

dynamically updating the spectrum heat map based on the received network quality data;

identifying a travel route for a selected one of the plurality of AVs;

using the updated spectrum heat map, identifying a plurality of the base stations and a corresponding plurality of network types with coverage along the travel route;

determining an optimal connection schedule for the selected AV, the optimal connection schedule indicating location points along the travel route at which the selected AV is to switch from a previous network connection to a succeeding network connection; and

transmitting the optimal connection schedule to the selected AV to enable the selected AV to switch connections at the location points along the travel route.

9. The method of claim 8, wherein the plurality of network types indicated on the spectrum heat map comprises one or more of a 3G, a 4G, a long-term evolution (LTE), or a WiFi network type.

10. The method of claim 9, wherein the plurality of network types indicated on the spectrum heat map further comprises one or more of a WiMax, a WiGig, or a dedicated short range communications (DSRC) network type.

11. The method of claim 10, wherein the plurality of network types indicated on the spectrum heat map further comprises a 900 MHz band network type.

12. The method of claim 8, further comprising:

receiving a pick-up request from a requesting user in the given region;

in response to receiving the pick-up request, identifying that the selected AV is proximate to a pick-up location identified in the pick-up request; and

instructing the selected AV to travel to the pick-up location to service the pick-up request.

13. The method of claim 12, wherein the pick-up request further includes a destination location, and wherein the travel route comprises a route from the pick-up location to the destination location.

14. The method of claim 8, wherein determining the optimal connection schedule comprises identifying, from the corresponding plurality of network types indicated on the spectrum heat map, a string of networks along the travel route that have a highest respective bandwidth.

15. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a backend system that manages a plurality of automated vehicles (AVs) over a given region, cause the backend system to:

store a spectrum heat map in a memory resource, the spectrum heat map indicating network coverage strength for a plurality of network types sourced at base stations located throughout the given region;

dynamically receive network quality data for each of the plurality of network types from the plurality of AVs traveling throughout the given region;

dynamically update the spectrum heat map based on the received network quality data;

identify a travel route for a selected one of the plurality of AVs;

using the updated spectrum heat map, identify a plurality of the base stations and a corresponding plurality of network types with coverage along the travel route;

determine an optimal connection schedule for the selected AV, the optimal connection schedule indicating location points along the travel route at which the selected AV is to switch from a previous network connection to a succeeding network connection; and

transmit the optimal connection schedule to the selected AV to enable the selected AV to switch connections at the location points along the travel route.

16. The non-transitory computer readable medium of claim 15, wherein the executed instructions further cause the backend system to:

receive a pick-up request from a requesting user in the given region;

in response to receiving the pick-up request, identify that the selected AV is proximate to a pick-up location identified in the pick-up request; and

instruct the selected AV to travel to the pick-up location to service the pick-up request.

17. The non-transitory computer readable medium of claim 16, wherein the pick-up request further includes a destination location, and wherein the travel route comprises a route from the pick-up location to the destination location.