Backend system for route planning of autonomous vehicles

• US 9,557,183 B1
• Filed: 12/08/2015
• Issued: 01/31/2017
• Est. Priority Date: 12/08/2015
• Status: Active Grant

• Current Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Ross, William, Aitken, Michael
• Primary Examiner(s): Do, Truc M
• Assistant Examiner(s): Whittington, Jess
• Application Number:
  US14/962,974
• Time in Patent Office: 420 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/096816 where the complete route is transmitted to the vehicle at once
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• 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 84/042 Public Land Mobile systems, e.g. cellular systems
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A backend system can store a network resource map that indicates network coverages areas for a plurality of base stations over a given region. The system can receive a pick-up request from a requesting user seeking transportation from a pick-up location to a destination, and instruct an automated vehicle (AV) to service the pick-up request. The system can further determine a plurality of possible routes from the pick-up location to the destination, and perform an optimization operation to determine an optimal route by utilizing the network resource map. The system can then transmit route data for the optimal route to the selected AV.

Claims

1. A backend system comprising:

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;

manage transportation for a fleet of autonomous vehicles (AVs) within a given region;

access a network resource map that indicates network coverage areas for a plurality of base stations over the given region;

receive a pick-up request from a requesting user seeking transportation from a pick-up location to a destination;

instruct a selected AV to service the pick-up request;

determine a plurality of possible routes from the pick-up location to the destination;

perform an optimization operation to determine an optimal one of the plurality of possible routes by utilizing the network resource map;

transmit route data for the optimal route to the selected AV;

using the network resource map, identify one or more locations along the optimal route with limited network availability;

select one or more additional AVs in the fleet of AVs to establish a mesh network with the selected AV across the one or more locations along the optimal route; and

generate a time table indicating when the selected AV will reach each of the one or more locations;

wherein the executed instructions cause the one or more processors to instruct the one or more additional AVs to reach the one or more locations based on the generated time table for the selected AV.

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

predict communication requirements for the selected AV between the pick-up location and the destination;

wherein the executed instructions cause the one or more processors to determine the optimal route based on (i) results of the optimization operation, and (ii) the predicted communications requirements for the selected AV.

3. The backend system of claim 2, wherein the executed instructions further cause the backend system to:

transmit network configuration data to the selected AV to configure an on-board communication system for data communications along the optimal route.

4. The backend system of claim 3, wherein the network configuration data causes the selected AV to switch between a plurality of communication channels along the optimal route in order to optimize communications.

5. The backend system of claim 4, wherein the plurality of communication channels comprise (i) at least one of a 3G, a 4G, or a long-term evolution (LTE) channel, and (ii) at least one of a WiFi or a WiMax channel.

6. The backend system of claim 5, wherein the plurality of communication channels further comprise at least one of a dedicated short-range communications (DSRC) channel, a mesh networking channel, or a WiGig channel.

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

collect network latency data from the fleet of AVs to update the network resource map.

8. The backend system of claim 7, wherein the executed instructions further cause the backend system to:

collect cost data from the fleet of AVs, the cost data indicating costs associated with connecting with and transmitting data over communication networks throughout the given region.

9. The backend system of claim 8, wherein the executed instructions cause the backend system to determine the optimal route based on the network resource map and the cost data.

10. The backend system of claim 8, wherein the executed instructions further cause the one or more processors to:

continuously update the network latency data and the cost data for the given region;

map a number of default routes between high traffic destinations throughout the given region based on the updated network latency data and the updated cost data; and

automatically instruct AVs along the default routes for pick-up requests that match the high traffic destinations.

11. A computer-implemented method for facilitating transportation, the method being performed by one or more processors of a backend system and comprising:

managing transportation for a fleet of autonomous vehicles (AVs) within a given region;

accessing a network resource map that indicates network coverage areas for a plurality of base stations over the given region;

receiving a pick-up request from a requesting user seeking transportation from a pick-up location to a destination;

instructing a selected AV to service the pick-up request;

determining a plurality of possible routes from the pick-up location to the destination;

performing an optimization operation to determine an optimal one of the plurality of possible routes by utilizing the network resource map;

transmitting route data for the optimal route to the selected AV;

using the network resource map, identifying one or more locations along the optimal route with limited network availability;

selecting one or more additional AVs in the fleet of AVs to establish a mesh network with the selected AV across the one or more locations along the optimal route; and

generating a time table indicating when the selected AV will reach each of the one or more locations;

wherein the one or more processors of the backend system instruct the one or more additional AVs to reach the one or more locations based on the generated time table for the selected AV.

12. The method of claim 11, further comprising:

predicting communication requirements for the selected AV between the pick-up location and the destination;

wherein the backend system determines the optimal route based on (i) results of the optimization operation, and (ii) the predicted communications requirements for the selected AV.

13. The method of claim 12, further comprising:

transmitting network configuration data to the selected AV to configure an on-board communication system for data communications along the optimal route.

14. The method of claim 13, wherein the network configuration data causes the selected AV to switch between a plurality of communication channels along the optimal route in order to optimize communications.

15. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a backend system, cause the backend system to:

manage transportation for a fleet of autonomous vehicles (AVs) within a given region;

access a network resource map that indicates network coverage areas for a plurality of base stations over the given region;

collect network latency data from the fleet of AVs to update the network resource map;

collect cost data from the fleet of AVs, the cost data indicating costs associated with connecting with and transmitting data over communication networks throughout the given region;

receive a pick-up request from a requesting user seeking transportation from a pick-up location to a destination;

instruct a selected AV to service the pick-up request;

determine a plurality of possible routes from the pick-up location to the destination;

perform an optimization operation to determine an optimal one of the plurality of possible routes by utilizing the network resource map;

transmit route data for the optimal route to the selected AV;

continuously update the network latency data and the cost data for the given region;

map a number of optimal default routes between high traffic destinations throughout the given region based on the updated network latency data and the updated cost data; and

automatically instruct AVs along the optimal default routes for pick-up requests that match the high traffic destinations.

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

predict communication requirements for the selected AV between the pick-up location and the destination;

wherein the executed instructions cause the one or more processors to determine the optimal route based on (i) results of the optimization operation, and (ii) the predicted communications requirements for the selected AV.

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

transmit network configuration data to the selected AV to configure an on-board communication system for data communications along the optimal route.