Kinematic path prediction of vehicles on curved paths

• US 9,620,015 B2
• Filed: 07/13/2015
• Issued: 04/11/2017
• Est. Priority Date: 07/13/2015
• Status: Active Grant

• Current Assignee: Nissan Motor Co., Ltd.
• Original Assignee: Nissan North America Incorporated (Nissan Motor Co., Ltd.)
• Inventors: Probert, Neal, Goudy, Roy, Christensen, Andrew, Chambers, Jeremy
• Primary Examiner(s): ARTHUR JEANGLAUD, GERTRUDE
• Application Number:
  US14/797,402
• Publication Number:
  US 20170018186A1
• Time in Patent Office: 638 Days
• Field of Search: 701 2, 701 25, 701 96, 701301, 701400, 701416, 701 292, 340903, 340905
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0289 with means for avoiding collisions between vehicles vehicle fittings for automatically controlling speed including means for detecting potential obstacles B60K31/0008; avoiding obstacles by action on the steering system B62D; radar, sonar, lidar systems designed for anti-collision purposes G01S13/93, G01S15/93, G01S17/93
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A host vehicle receives a remote vehicle message via a wireless electronic communication link. The remote vehicle message includes remote vehicle information indicating remote vehicle geospatial state information and remote vehicle kinematic state information that includes at least a remote heading angle for a remote vehicle. Host vehicle information is identified that includes host vehicle geospatial state information and host vehicle kinematic state information that includes at least a host heading angle for the host vehicle. Projected vehicle transportation network information representing a portion of the vehicle transportation network is generated based on the remote and host vehicle information, the portion including a curved path having a substantially constant radius. An expected host vehicle route for the host vehicle is determined in response to generating the projected vehicle transportation network information representing the curved path, and the host vehicle traverses the curved path using the expected host vehicle route.

Claims

1. A method of generating projected vehicle transportation network information for use in traversing a vehicle transportation network, the method comprising:

receiving, at a host vehicle, from a remote vehicle, via a wireless electronic communication link, a remote vehicle message, the remote vehicle message including remote vehicle information, the remote vehicle information indicating remote vehicle geospatial state information for the remote vehicle and remote vehicle kinematic state information for the remote vehicle, the remote vehicle kinematic state information including at least a remote heading angle for the remote vehicle;

identifying host vehicle information for the host vehicle, the host vehicle information including host vehicle geospatial state information and host vehicle kinematic state information for the host vehicle, the host vehicle kinematic state information including at least a host heading angle for the host vehicle;

generating, by a processor in response to instructions stored on a non-transitory computer readable medium, projected vehicle transportation network information representing a portion of the vehicle transportation network based on the remote vehicle information and the host vehicle information, the portion including a curved path having a substantially constant radius determined using the host heading angle for the host vehicle and the remote heading angle for the remote vehicle;

determining an expected host vehicle route for the host vehicle in response to generating the projected vehicle transportation network information representing the curved path; and

traversing, by the host vehicle, the curved path using the expected host vehicle route.

2. The method of claim 1, wherein receiving the remote vehicle message includes:

storing the remote vehicle information in a memory of the host vehicle.

3. The method of claim 1, wherein the remote vehicle geospatial state information includes geospatial coordinates for the remote vehicle, and the remote vehicle kinematic state information includes at least one of a remote vehicle velocity for the remote vehicle or a remote vehicle acceleration for the remote vehicle.

7. The method of claim 1, wherein generating the projected vehicle transportation network information includes:

generating a remote radius of curvature of a travel path for the remote vehicle using the remote heading angle;

generating a host radius of curvature of a travel path for the host vehicle using the host heading angle; and

generating a length of the curved path between the host vehicle and the remote vehicle using the remote radius of curvature, the host radius of curvature, the remote vehicle geospatial state information, the host vehicle geospatial state information, and an angle between the remote radius of curvature and the host radius of curvature.

8. The method of claim 1, wherein determining the expected host vehicle route for the host vehicle comprises generating an expected position of the host vehicle along the curved path at a future point in time.

9. The method of claim 1, further comprising:

identifying a remote vehicle expected path for the remote vehicle based on the remote vehicle information;

identifying a host vehicle expected path for the host vehicle based on the host vehicle information; and

determining a distance between the remote vehicle and the host vehicle along the curved path.

10. The method of claim 1, wherein generating the projected vehicle transportation network information includes, for each of a plurality of possible angular ranges for each of the remote heading angle and the host heading angle:

determining a geodesic between the host vehicle and the remote vehicle;

determining a remote radius of curvature of a travel path for the remote vehicle using the remote heading angle and an assumption regarding a curving direction of the curved path;

determining a host radius of curvature of a travel path for the host vehicle using the host heading angle and the assumption regarding the curving direction of the curved path;

determining a convergence angle of the geodesic;

determining a first host angle between the geodesic and the host radius of curvature based on the convergence angle and the host heading angle assuming the curving direction is left;

determining a second host angle between the geodesic and the host radius of curvature based on the convergence angle and the host heading angle assuming the curving direction is right;

determining a first remote angle between the geodesic and the remote radius of curvature based on the convergence angle and the remote heading angle assuming the curving direction is left;

determining a second remote angle between the geodesic and the remote radius of curvature based on the convergence angle and the remote heading angle assuming the curving direction is right;

determining a first facing angle between the remote radius of curvature and the host radius of curvature based on the first host angle and the first remote angle;

determining a second facing angle between the remote radius of curvature and the host radius of curvature based on the second host angle and the second remote angle;

determining a left probability operator using the first facing angle;

determining a right probability operator using the second facing angle;

determining at least one of a final host angle between the geodesic and the host radius of curvature based on the left probability operator and the right probability operator or a final remote angle between the geodesic and the remote radius of curvature based on the left probability operator and the right probability operator;

determining a final facing angle between the remote radius of curvature and the host radius of curvature based on the left probability operator and the right probability operator; and

determining a path length of the curved path between the host vehicle and the remote vehicle using the final facing angle and the at least one of the final host angle or the final remote angle.

11. The method of claim 10, wherein the convergence angle is an angle between the geodesic and a reference direction relative to the host vehicle in a geospatial domain.

12. The method of claim 10, wherein the final facing angle is an angle of greater than π

radians.

13. The method of claim 10, wherein determining the path length comprises determining the path length using the final facing angle, the at least one of the final host angle or the final remote angle, the remote vehicle geospatial state information and the host vehicle geospatial state information.

14. The method of claim 1, wherein generating the projected vehicle transportation network information includes:

storing the projected vehicle transportation network information as a projected vehicle transportation network information record, the projected vehicle transportation network information record including current temporal information.

4. A method of generating projected vehicle transportation network information for use in traversing a vehicle transportation network, the method comprising:

receiving, at a host vehicle, from a remote vehicle, via a wireless electronic communication link, a remote vehicle message, the remote vehicle message including remote vehicle information, the remote vehicle information indicating remote vehicle geospatial state information for the remote vehicle and remote vehicle kinematic state information for the remote vehicle, the remote vehicle kinematic state information including at least a remote heading angle for the remote vehicle;

identifying host vehicle information for the host vehicle, the host vehicle information including host vehicle geospatial state information and host vehicle kinematic state information for the host vehicle, the host vehicle kinematic state information including at least a host heading angle for the host vehicle;

generating, by a processor in response to instructions stored on a non-transitory computer readable medium, projected vehicle transportation network information representing a portion of the vehicle transportation network based on the remote vehicle information and the host vehicle information, the portion including a curved path having a substantially constant radius;

determining an expected host vehicle route for the host vehicle in response to generating the projected vehicle transportation network information representing the curved path; and

traversing, by the host vehicle, the curved path using the expected host vehicle route, wherein generating the projected vehicle transportation network information includes;

generating a remote radius of curvature of a travel path for the remote vehicle using the remote heading angle and the remote vehicle geospatial state information;

generating a host radius of curvature of a travel path for the host vehicle using the host heading angle and the host vehicle geospatial state information; and

generating the curved path using the remote radius of curvature, the host radius of curvature, and an angle between the remote radius of curvature and the host radius of curvature.

5. The method of claim 4, wherein the host vehicle geospatial state information comprises Global Positioning System (GPS) coordinates for a latitude and a longitude of the host vehicle, and the remote vehicle geospatial state information comprises GPS coordinates for a latitude and a longitude of the remote vehicle.

6. The method of claim 4, wherein receiving the remote vehicle message comprises periodically receiving values for the remote heading angle and the remote vehicle geospatial state information, the method further comprising:

comparing the remote radius of curvature for the remote vehicle for each of the periodically received values of the remote heading angle and the remote vehicle geospatial state information with a respective host radius of curvature generated from concurrent values of the host heading angle and the host vehicle geospatial state information, each remote radius of curvature and host radius of curvature forming a pair of values; and

generating the projected vehicle transportation network information comprises;

omitting any of the pairs of values whose value for the remote radius of curvature is different from the value for the host radius of curvature by a predetermined amount.

15. A host vehicle comprising:

a processor configured to execute instructions stored on a non-transitory computer readable medium to generate projected vehicle transportation network information for use in traversing a vehicle transportation network by;

receiving, from a remote vehicle, via a wireless electronic communication link, a remote vehicle message, the remote vehicle message including remote vehicle information, the remote vehicle information indicating remote vehicle geospatial state information for the remote vehicle and remote vehicle kinematic state information for the remote vehicle, the remote vehicle kinematic state information including at least a remote heading angle for the remote vehicle;

identifying host vehicle information for the host vehicle, the host vehicle information including host vehicle geospatial state information and host vehicle kinematic state information for the host vehicle, the host vehicle kinematic state information including at least a host heading angle for the host vehicle;

generating projected vehicle transportation network information representing a portion of the vehicle transportation network based on the remote vehicle information and the host vehicle information, the portion including a curved path having a substantially constant radius determined using the host heading angle for the host vehicle and the remote heading angle for the remote vehicle;

determining an expected host vehicle route for the host vehicle in response to generating the projected vehicle transportation network information representing the curved path; and

traversing the curved path using the expected host vehicle route.

16. The host vehicle of claim 15, wherein generating the projected vehicle transportation network information includes:

defining a direction of the curved path relative to a reference direction.

17. The host vehicle of claim 15, wherein generating the projected vehicle transportation network information includes calculating a path length between the host vehicle and the remote vehicle along the curved path by:

determining a length of a geodesic between the host vehicle and the remote vehicle;

determining a host angle between the geodesic and a host radius of curvature, the host radius of curvature based on the host heading angle;

determining a remote angle between the geodesic and a remote radius of curvature, the remote radius of curvature based on the remote heading angle;

determining a facing angle between the remote radius of curvature and the host radius of curvature based on the host angle and remote angle; and

determining the path length of the curved path between the host vehicle and the remote vehicle using the host angle, the remote vehicle geospatial state information, the host vehicle geospatial state information, the length of the geodesic and at least one of the remote angle or the host angle.

18. The host vehicle of claim 17, wherein calculating a path length between the host vehicle and the remote vehicle along the curved path comprises:

determining a left probability operator that indicates whether a curving direction of the curved path relative to a reference direction is a left direction; and

determining a right probability operator that indicates whether the curving direction of the curved path relative to the reference direction is a right direction; and

wherein;

determining the host angle, determining the remote angle and determining the facing angle depend upon values of the left probability operator and the right probability operator.

19. The host vehicle of claim 18, wherein the processor is configured to determine a convergence angle of the geodesic;

and wherein;

determining the host angle between the geodesic and the host radius of curvature comprises;

determining a first host angle between the geodesic and the host radius of curvature based on the convergence angle and the host heading angle assuming the curving direction is left;

determining a second host angle between the geodesic and the host radius of curvature based on the convergence angle and the host heading angle assuming the curving direction is right; and

generating the host angle by combining the first host angle modified by the left probability operator with the second host angle modified by the right probability operator;

determining the remote angle between the geodesic and the remote radius of curvature comprises;

determining a first remote angle between the geodesic and the remote radius of curvature based on the convergence angle and the remote heading angle assuming the curving direction is left;

determining a second remote angle between the geodesic and the remote radius of curvature based on the convergence angle and the remote heading angle assuming the curving direction is right; and

generating the remote angle by combining the first remote angle modified by the left probability operator with the second remote angle modified by the right probability operator; and

determining the facing angle between the remote radius of curvature and the host radius of curvature comprises;

determining a first facing angle based on the first host angle and the first remote angle;

determining a first facing angle based on the first host angle and the first remote angle; and

generating the remote angle by combining the first facing angle modified by the left probability operator with the second facing angle modified by the right probability operator.

20. The host vehicle of claim 18, wherein the processor is configured to:

determine the left probability operator based on a plurality of values for the facing angle assuming the curving direction is left; and

determine the right probability operator based on a plurality of values for the facing angle assuming the curving direction is right.