Associating parking areas with destinations based on automatically identified associations between vehicle operating information and non-vehicle operating information

• US 9,568,335 B2
• Filed: 01/30/2015
• Issued: 02/14/2017
• Est. Priority Date: 01/30/2015
• Status: Active Grant

• Current Assignee: Nissan Motor Co., Ltd.
• Original Assignee: Nissan North America Incorporated (Nissan Motor Co., Ltd.)
• Inventors: Thakur, Siddharth, Saito, Daisuke
• Primary Examiner(s): Nguyen, Nga X
• Application Number:
  US14/609,745
• Publication Number:
  US 20160223345A1
• Time in Patent Office: 746 Days
• Field of Search: 701/408
• US Class Current: 1/1
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G01C 21/3685 the POI's being parking facilities
• CPC Class Codes: G05D 1/00 Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots drive control systems specially adapted for autonomous road vehicles B60W60/00
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0217 in accordance with energy consumption, time reduction or distance reduction criteria
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: G08G 1/0137 for specific applications

Abstract

A method and apparatus for associating parking areas with destinations based on automatically identified associations between vehicle operating information and non-vehicle operating information may include a vehicle identifying vehicle transportation network information including a primary destination, such that the vehicle transportation network information includes parking area information, and such that the parking area information includes parking area association information describing an association between at least one parking area from the plurality of parking areas and the primary destination based at least in part on an automatically identified association between vehicle operating information and non-vehicle operating information. The vehicle may determine a target parking area from the plurality of parking areas for the primary destination based on the vehicle transportation network information, and identify a route from an origin to the target parking area in the vehicle transportation network using the vehicle transportation network information.

Claims

1. A vehicle comprising:

a processor configured to execute instructions stored on a non-transitory computer readable medium to;

identify vehicle transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the vehicle transportation network information includes identifying the vehicle transportation network information such that the vehicle transportation network information includes parking area information representing a plurality of parking areas, wherein each parking area from the plurality of parking areas corresponds with a respective location in the vehicle transportation network, and such that the parking area information includes parking area association information describing an association between at least one parking area from the plurality of parking areas and the primary destination based at least in part on an automatically identified association between vehicle operating information and non-vehicle operating information, wherein the parking area information is based on operating information for a plurality of vehicles, wherein the operating information includes vehicle operating information and non-vehicle operating information, and wherein the vehicle operating information is information reported by the plurality of vehicles, wherein the vehicle operating information includes a plurality of vehicle operations, wherein each vehicle operation from the plurality of vehicle operations is associated with a respective vehicle from the plurality of vehicles, and wherein each parking location from the plurality of parking areas corresponds with a respective vehicle operation from the plurality of vehicle operations, the vehicle operating information includes passenger information that identifies a passenger and associates the passenger with a vehicle from the plurality of vehicles, the vehicle transportation network includes a plurality of destinations that includes the primary destination, and the non-vehicle operating information is information reported for a plurality of users, wherein the non-vehicle operating information includes a plurality of non-vehicle operations, wherein each non-vehicle operation from the plurality of non-vehicle operations is associated with a respective user from the plurality of users, wherein the plurality of non-vehicle operations includes a plurality of destination operations, wherein each destination operation from the plurality of destination operations corresponds with a respective destination from the plurality of destinations, and such that the parking area information is determined by generating a plurality of temporal operating information groups by temporally grouping the operating information, such that each temporal operating information group from the plurality of temporal operating information groups includes;

a respective plurality of temporally grouped vehicle operations, wherein each temporally grouped vehicle operation from the plurality of temporally grouped vehicle operations includes temporal information within a period identified for the temporal operating information group; and

a respective plurality of temporally grouped non-vehicle operations, wherein each temporally grouped non-vehicle operation from the plurality of temporally grouped non-vehicle operations includes temporal information within the period identified for the temporal operating information group;

determine a target parking area from the plurality of parking areas for the primary destination based on the vehicle transportation network information; and

identify a route from an origin to the target parking area in the vehicle transportation network using the vehicle transportation network information; and

a trajectory controller configured to operate the vehicle to travel from the origin to the target parking area using the route.

2. The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

identify a docking location for the primary destination; and

identify the route such that the route includes a first route portion from the origin to the docking location and a second route portion from the docking location to the target parking area.

3. The vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the vehicle transportation network information such that the parking area information is determined by, for each temporal operating information group from the plurality of temporal operating information groups, generating a plurality of spatial clusters.

4. The vehicle of claim 3, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the vehicle transportation network information such that the parking area information is determined by, for each spatial cluster from the plurality of spatial clusters from a temporal operating information group:

identifying a plurality of passenger identifiers, wherein each passenger identifier is associated with a respective vehicle operation;

identifying a plurality of user identifiers, wherein each user identifier is associated with a respective non-vehicle operation; and

identifying a plurality of distinct tuples, wherein each tuple includes a passenger identifier from the plurality of passenger identifiers and a user identifier from the plurality of user identifiers.

5. The vehicle of claim 4, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the vehicle transportation network information such that the parking area information is determined by identifying a plurality of candidate tuples from the plurality of distinct tuples by, for each distinct tuple from the plurality of distinct tuples:

identifying a temporal location for the non-vehicle operation corresponding to the user identifier from the distinct tuple;

identifying a period for the vehicle operation corresponding to the passenger identifier from the distinct tuple; and

on a condition that the temporal location is within the period, including the distinct tuple in the plurality of candidate tuples.

6. The vehicle of claim 5, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the vehicle transportation network information such that the parking area information is determined by, identifying a plurality of exclusive tuples by:

including each candidate tuple from the plurality of candidate tuples in the plurality of exclusive tuples, such that the plurality of exclusive tuples includes tuples from each spatial cluster from the plurality of spatial clusters from each temporal operating information group from the plurality of temporal operating information groups; and

for each candidate tuple from the plurality of exclusive tuples;

omitting the candidate tuple from the plurality of exclusive tuples on a condition that another candidate tuple from the plurality of exclusive tuples includes the passenger identifier from the candidate tuple and includes a user identifier that differs from the user identifier for the candidate tuple; and

omitting the candidate tuple from the plurality of exclusive tuples on a condition that another candidate tuple from the plurality of exclusive tuples includes the user identifier from the candidate tuple and includes a passenger identifier that differs from the passenger identifier for the candidate tuple.

7. The vehicle of claim 6, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the vehicle transportation network information such that the parking area information is determined by,

identifying a cardinality of a set of exclusive tuples from the plurality of exclusive tuples, wherein the passenger identifier for each exclusive tuples from the plurality of exclusive tuples matches the passenger identifier for each other exclusive tuples from the plurality of exclusive tuples, and wherein the user identifier for each exclusive tuples from the plurality of exclusive tuples matches the user identifier for each other exclusive tuples from the plurality of exclusive tuples,

automatically identifying associations between vehicle operating information and non-vehicle operating information by associating the passenger identifier with the user identifier on a condition that the cardinality exceeds an association threshold.

8. The vehicle of claim 1, wherein the vehicle is an autonomous vehicle.

9. A vehicle comprising:

a processor configured to execute instructions stored on a non-transitory computer readable medium to;

identify vehicle transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the vehicle transportation network information includes identifying the vehicle transportation network information such that the vehicle transportation network information includes parking area information representing a plurality of parking areas, wherein each parking area from the plurality of parking areas corresponds with a respective location in the vehicle transportation network, and such that the parking area information includes parking area association information describing an association between at least one parking area from the plurality of parking areas and the primary destination based at least in part on an automatically identified association between vehicle operating information and non-vehicle operating information, wherein the parking area information is based on operating information for a plurality of vehicles, and wherein the operating information includes vehicle operating information and non-vehicle operating information, wherein the vehicle operating information is information reported by the plurality of vehicles, wherein the vehicle operating information includes a plurality of vehicle operations, wherein each vehicle operation from the plurality of vehicle operations is associated with a respective vehicle from the plurality of vehicles, and wherein each parking location from the plurality of parking areas corresponds with a respective vehicle operation from the plurality of vehicle operations, and wherein the vehicle operating information includes passenger information that identifies a passenger and associates the passenger with a vehicle from the plurality of vehicles, and wherein the non-vehicle operating information is information reported for a plurality of users, wherein the non-vehicle operating information includes a plurality of non-vehicle operations, wherein each non-vehicle operation from the plurality of non-vehicle operations is associated with a respective user from the plurality of users, and wherein the plurality of non-vehicle operations includes a plurality of destination operations, wherein each destination operation from the plurality of destination operations corresponds with a respective destination from the plurality of destinations, such that each temporal operating information group from the plurality of temporal operating information groups includes;

a respective plurality of temporally grouped vehicle operations, wherein each temporally grouped vehicle operation from the plurality of temporally grouped vehicle operations includes temporal information within a period identified for the temporal operating information group; and

a respective plurality of temporally grouped non-vehicle operations, wherein each temporally grouped non-vehicle operation from the plurality of temporally grouped non-vehicle operations includes temporal information within the period identified for the temporal operating information group;

determine a target parking area from the plurality of parking areas for the primary destination based on the vehicle transportation network information; and

identify a route from an origin to the target parking area in the vehicle transportation network using the vehicle transportation network information; and

a trajectory controller configured to operate the vehicle to travel from the origin to the target parking area using the route.

10. The vehicle of claim 9, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

identify a docking location for the primary destination; and

identify the route such that the route includes a first route portion from the origin to the docking location and a second route portion from the docking location to the target parking area.

11. The vehicle of claim 9, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the vehicle transportation network information such that the parking area information is determined by automatically identifying associations between the vehicle operating information and the non-vehicle operating information by:

for each temporal operating information group from the plurality of temporal operating information groups, generating a plurality of spatial clusters;

for each spatial cluster from the plurality of spatial clusters from a temporal operating information group;

identifying a plurality of passenger identifiers, wherein each passenger identifier is associated with a respective vehicle operation,

identifying a plurality of user identifiers, wherein each user identifier is associated with a respective non-vehicle operation,

identifying a plurality of distinct tuples, wherein each tuple includes a passenger identifier from the plurality of passenger identifiers and a user identifier from the plurality of user identifiers, and

identifying a plurality of candidates tuples from the plurality of distinct tuples by, for each distinct tuple from the plurality of distinct tuples;

identifying a temporal location for the non vehicle operation corresponding to the user identifier from the distinct tuple;

identifying a period for the vehicle operation corresponding to the passenger identifier from the distinct tuple; and

on a condition that the temporal location is within the period, including the distinct tuple in the plurality of candidate tuples;

identifying a plurality of exclusive tuples by;

including each candidate tuple from each plurality of candidate tuples from each spatial cluster from each plurality of spatial clusters from each temporal operating information group from the plurality of temporal operating information groups in the plurality of exclusive tuples, and

for each candidate tuple from the plurality of exclusive tuples;

omitting the candidate tuple from the plurality of exclusive tuples on a condition that another candidate tuple from the plurality of exclusive tuples includes the passenger identifier from the candidate tuple and includes a user identifier that differs from the user identifier for the candidate tuple; and

omitting the candidate tuple from the plurality of exclusive tuples on a condition that another candidate tuple from the plurality of exclusive tuples includes the user identifier from the candidate tuple and includes a passenger identifier that differs from the passenger identifier for the candidate tuple;

generating a plurality of exclusive tuple groups, such that the passenger identifier for each exclusive tuple for a respective exclusive tuple group from the plurality of exclusive tuple groups matches each other passenger identifier for the respective exclusive tuple group, and such that the user identifier for each exclusive tuple for the respective exclusive tuple group matches each other user identifier for the respective exclusive tuple group;

identifying a cardinality of each exclusive tuple group from the plurality of exclusive tuple groups; and

associating the passenger identifier from a respective exclusive tuple group with the user identifier from the exclusive tuple group on a condition that the cardinality of the exclusive tuple group exceeds an association threshold.

12. The vehicle of claim 9, wherein the vehicle is an autonomous vehicle.