Associating passenger docking locations with destinations using vehicle transportation network partitioning

• US 9,436,183 B2
• Filed: 01/15/2015
• Issued: 09/06/2016
• Est. Priority Date: 01/15/2015
• Status: Active Grant

• Current Assignee: Nissan Motor Co., Ltd.
• Original Assignee: Nissan North America Incorporated (Nissan Motor Co., Ltd.)
• Inventors: Thakur, Siddharth Kumar, Kojo, Naoki, Meuleau, Nicolas, Saito, Daisuke, Lele, Nikhil
• Primary Examiner(s): Kan, Yuri
• Application Number:
  US14/597,539
• Publication Number:
  US 20160209842A1
• Time in Patent Office: 600 Days
• Field of Search: 701/24, 701/25, 701/26, 701/408, 701/2, 705/14.23, 705/337, 705/7.14
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• 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 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30

Abstract

A method and apparatus for associating passenger docking locations with destinations using vehicle transportation network partitioning are disclosed. Associating passenger docking locations with destinations using vehicle transportation network partitioning may include an autonomous vehicle identifying transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the transportation network information includes identifying the transportation network information such that it includes docking location information representing a plurality of docking locations, wherein each docking location corresponds with a respective location in the vehicle transportation network, such that at least one docking location is associated with the primary destination based on pedestrian travel time, determining a target docking location, identifying a route from an origin to the target docking location in the vehicle transportation network using the transportation network information, and traveling from the origin to the target docking location using the route.

Claims

1. An autonomous vehicle comprising:

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

identify transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the transportation network information includes identifying the transportation network information such that the transportation network information includes docking location information representing a plurality of docking locations, wherein each docking location from the plurality of docking locations corresponds with a respective location in the vehicle transportation network, and such that at least one docking location from the plurality of docking locations is associated with the primary destination based on pedestrian travel time,

determine a target docking location from the plurality of docking locations for the primary destination based on the transportation network information, and

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

a trajectory controller configured to operate the autonomous vehicle to travel from the origin to the target docking location using the route.

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

in response to performing a docking operation at the target docking location, identify a secondary destination in the vehicle transportation network, wherein the secondary destination is a parking area associated with the primary destination, and wherein the trajectory controller is configured to operate the autonomous vehicle to park at the secondary destination;

identify a subsequent route from the target docking location to the secondary destination in the vehicle transportation network using the transportation network information, and wherein the trajectory controller is configured to operate the autonomous vehicle to travel from the target docking location to the secondary destination using the route.

3. The autonomous vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the docking location information is based on operating information for a plurality of vehicles, wherein the operating information includes a plurality of operations, wherein each operation from the plurality of operations is associated with a respective vehicle from the plurality of vehicles, and wherein each docking location from the plurality of docking locations corresponds with a respective operation from the plurality of operations.

4. The autonomous vehicle of claim 1, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the transportation network information includes road segment information representing a plurality of road segments in the vehicle transportation network.

5. The autonomous vehicle of claim 4, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the target docking location is associated with the primary destination by associating each docking location from the plurality of docking locations with a respective road segment from the plurality of road segments.

6. The autonomous vehicle of claim 5, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the target docking location is associated with the primary destination by identifying a plurality of docking location clusters within respective road segments from the plurality of road segments, wherein each docking location cluster from the plurality of docking location clusters includes at least one docking location from the plurality of docking locations.

7. The autonomous vehicle of claim 6, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the target docking location is associated with the primary destination by identifying a plurality of docking location cluster medians, wherein each docking location cluster median form the plurality of docking location cluster medians indicates a median of a respective docking location cluster from the plurality of docking location clusters.

8. The autonomous vehicle of claim 7, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the target docking location is associated with the primary destination by partitioning the vehicle transportation network to generate a plurality of vehicle transportation network regions based on the plurality of docking location cluster medians.

9. The autonomous vehicle of claim 8, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that partitioning the vehicle transportation network includes partitioning the vehicle transportation network such that each vehicle transportation network region from the plurality of vehicle transportation network regions includes a docking location cluster median form the plurality of docking location cluster medians.

10. The autonomous vehicle of claim 9, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that partitioning the vehicle transportation network includes partitioning the vehicle transportation network such that each vehicle transportation network region from the plurality of vehicle transportation network regions includes a plurality of location points from the vehicle transportation network, wherein a respective distance between each location point from the plurality of location points and the docking location cluster median associated with the vehicle transportation network region is within the respective distance between the location point and each docking location cluster median from the plurality of docking location cluster medians.

11. The autonomous vehicle of claim 10, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that partitioning the vehicle transportation network includes partitioning the vehicle transportation network such that a distance between a location point from the plurality of location points and a docking location cluster median is an estimated pedestrian travel time.

12. The autonomous vehicle of claim 11, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the target docking location from the plurality of docking locations is associated with the primary destination by:

identifying an entrance location for the primary destination in a vehicle transportation network region from the plurality of vehicle transportation network regions, wherein the vehicle transportation network region includes the primary destination; and

associating the entrance location with a docking location cluster median corresponding to the vehicle transportation network region.

13. The autonomous vehicle of claim 12, wherein identifying the entrance location includes:

identifying, as the entrance location, a defined entrance location indicated in the transportation network information, on a condition that the defined entrance location is indicated in the transportation network information; and

generating, as the entrance location, a predicted entrance location for the primary destination, on a condition that the transportation network information omits the defined entrance location for the primary destination.

14. The autonomous vehicle of claim 13, wherein generating the predicted entrance location includes:

identifying a set of road segments from the plurality of road segments, wherein each road segment from the set of road segments is proximal to the primary destination;

generating a set of lines, wherein each line from the set of lines is perpendicular to a road segment from the set of road segments and intersects with the primary destination; and

identifying candidate predicted entrance locations, wherein each candidate predicted entrance location indicates an intersection of a line from the set of lines with a road segment from the set of road segments.

15. The autonomous vehicle of claim 14, wherein, on a condition that generating the predicted entrance location includes identifying a plurality of candidate predicted entrance locations, generating the predicted entrance location includes:

for each docking location cluster median in the vehicle transportation network region that includes the primary destination, ordering the plurality of candidate predicted entrance locations based on pedestrian travel time between the docking location cluster median and the respective predicted entrance location.

16. An autonomous vehicle comprising:

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

identify transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the transportation network information includes identifying the transportation network information such that the transportation network information includes docking location information representing a plurality of docking locations, wherein each docking location from the plurality of docking locations corresponds with a respective location in the vehicle transportation network, and road segment information representing a plurality of road segments in the vehicle transportation network, and such that a target docking location from the plurality of docking locations is associated with the primary destination based on pedestrian travel time, wherein the target docking location is associated with the primary destination based on pedestrian travel time by;

associating each docking location from the plurality of docking locations with a respective road segment from the plurality of road segments;

identifying a plurality of docking location clusters within respective road segments from the plurality of road segments, wherein each docking location cluster from the plurality of docking location clusters includes at least one docking location from the plurality of docking locations;

identifying a plurality of docking location cluster medians, wherein each docking location cluster median form the plurality of docking location cluster medians indicates a median of a respective docking location cluster from the plurality of docking location clusters; and

partitioning the vehicle transportation network to generate the plurality of vehicle transportation network regions based on the plurality of docking location cluster medians such that each vehicle transportation network region from the plurality of vehicle transportation network regions includes a docking location cluster median, and such that each vehicle transportation network region from the plurality of vehicle transportation network regions includes a plurality of location points from the vehicle transportation network, wherein a respective estimated pedestrian travel time between each location point from the plurality of location points and the docking location cluster median associated with the vehicle transportation network region is within the respective estimated pedestrian travel time between the location point and each docking location cluster median from the plurality of docking location cluster medians,

determine whether to use the target docking location based on the transportation network information, and

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

a trajectory controller configured to operate the autonomous vehicle to travel from the origin to the target docking location using the route.

17. The autonomous vehicle of claim 16, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

in response to performing a docking operation at the target docking location, identify a secondary destination in the vehicle transportation network, wherein the secondary destination is a parking area associated with the primary destination, and wherein the trajectory controller is configured to operate the autonomous vehicle to park at the secondary destination;

identify a subsequent route from the target docking location to the secondary destination in the vehicle transportation network using the transportation network information, and wherein the trajectory controller is configured to operate the autonomous vehicle to travel from the target docking location to the secondary destination using the route.

18. The autonomous vehicle of claim 16, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the docking location information is based on operating information for a plurality of vehicles, wherein the operating information includes a plurality of operations, wherein each operation from the plurality of operations is associated with a respective vehicle from the plurality of vehicles, and wherein each docking location from the plurality of docking locations corresponds with a respective operation from the plurality of operations.

19. The autonomous vehicle of claim 16, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the target docking location from the plurality of docking locations is associated with the primary destination by:

identifying an entrance location for the primary destination in a vehicle transportation network region from the plurality of vehicle transportation network regions, wherein the vehicle transportation network region includes the primary destination; and

associating the entrance location with a docking location cluster median corresponding to the vehicle transportation network region.

20. The autonomous vehicle of claim 19, wherein identifying the entrance location includes:

identifying, as the entrance location, a defined entrance location indicated in the transportation network information, on a condition that the defined entrance location is indicated in the transportation network information; and

generating, as the entrance location, a predicted entrance location for the primary destination, on a condition that the transportation network information omits a defined entrance location for the primary destination.

21. The autonomous vehicle of claim 20, wherein generating the predicted entrance location includes:

identifying a set of road segments from the plurality of road segments, wherein each road segment from the set of road segments is proximal to the primary destination;

generating a set of lines, wherein each line from the set of lines is perpendicular to a road segment from the set of road segments and intersects with the primary destination; and

identifying candidate predicted entrance locations, wherein each candidate predicted entrance location indicates an intersection of a line from the set of lines with a road segment from the set of road segments.

22. The autonomous vehicle of claim 21, wherein, on a condition that generating the predicted entrance location includes identifying a plurality of candidate predicted entrance locations, generating the predicted entrance location includes:

for each docking location cluster median in the vehicle transportation network region that includes the primary destination, ordering the plurality of candidate predicted entrance locations based on pedestrian travel time between the docking location cluster median and the respective predicted entrance location.

23. An autonomous vehicle comprising:

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

identify transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the transportation network information includes identifying the transportation network information such that the transportation network information includes docking location information representing a plurality of docking locations and road segment information representing a plurality of road segments, wherein each docking location from the plurality of docking locations corresponds with a respective location in the vehicle transportation network, and such that a docking location from the plurality of docking locations is associated with the primary destination based on pedestrian travel time, wherein the target docking location is associated with the primary destination based on pedestrian travel time by;

associating each docking location from the plurality of docking locations with a respective road segment from the plurality of road segments;

identifying a plurality of docking location clusters within respective road segments from the plurality of road segments, wherein each docking location cluster from the plurality of docking location clusters includes at least one docking location from the plurality of docking locations;

identifying a plurality of docking location cluster medians, wherein each docking location cluster median form the plurality of docking location cluster medians indicates a median of a respective docking location cluster from the plurality of docking location clusters;

partitioning the vehicle transportation network to generate the plurality of vehicle transportation network regions based on the plurality of docking location cluster medians such that each vehicle transportation network region from the plurality of vehicle transportation network regions includes a docking location cluster median from the plurality of docking location cluster medians, and such that each vehicle transportation network region from the plurality of vehicle transportation network regions includes a plurality of location points from the vehicle transportation network, wherein a respective estimated pedestrian travel time between each location point from the respective plurality of location points and the docking location cluster median associated with the vehicle transportation network region is within the respective estimated pedestrian travel time between the location point and each respective docking location cluster median from the plurality of docking location cluster medians;

identifying a defined entrance location in the vehicle transportation network as the primary destination location, on a condition that the transportation network information includes entrance location information identifying the defined entrance location;

generating a predicted entrance location as the primary destination location, on a condition that the transportation network information omits entrance location information identifying the defined entrance location, wherein generating the predicted entrance location includes;

identifying a set of road segments from the plurality of road segments, wherein each road segment from the set of road segments is proximal to the primary destination,

generating a set of lines, wherein each line from the set of lines is perpendicular to a road segment from the set of road segments and intersects with the primary destination, and

identifying candidate predicted entrance locations, wherein each candidate predicted entrance location indicates an intersection of a line from the set of lines with a road segment from the set of road segments, wherein, on a condition that generating the predicted entrance location includes identifying a plurality of candidate predicted entrance locations, generating the predicted entrance location includes, for each docking location cluster median in the vehicle transportation network region that includes the primary destination, ordering the plurality of candidate predicted entrance locations based on pedestrian travel time between the docking location cluster median and the respective predicted entrance location; and

associating the primary destination location with a docking location cluster median from the vehicle transportation network region,

determine whether to use the target docking location based on the transportation network information, and

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

a trajectory controller configured to operate the autonomous vehicle to travel from the origin to the target docking location using the route.

24. The autonomous vehicle of claim 23, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to:

in response to performing a docking operation at the target docking location, identify a secondary destination in the vehicle transportation network, wherein the secondary destination is a parking area associated with the primary destination, and wherein the trajectory controller is configured to operate the autonomous vehicle to park at the secondary destination;

identify a subsequent route from the target docking location to the secondary destination in the vehicle transportation network using the transportation network information, and wherein the trajectory controller is configured to operate the autonomous vehicle to travel from the target docking location to the secondary destination using the route.

25. The autonomous vehicle of claim 23, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium to identify the transportation network information such that the docking location information is based on operating information for a plurality of vehicles, wherein the operating information includes a plurality of operations, wherein each operation from the plurality of operations is associated with a respective vehicle from the plurality of vehicles, and wherein each docking location from the plurality of docking locations corresponds with a respective operation from the plurality of operations.