Lane level traffic information and navigation

• US 10,417,906 B2
• Filed: 12/23/2016
• Issued: 09/17/2019
• Est. Priority Date: 12/23/2016
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: Fowe, James, Bernhardt, Bruce, Pellolio, Filippo
• Primary Examiner(s): Brushaber, Frederick M
• Application Number:
  US15/389,500
• Publication Number:
  US 20180182238A1
• Time in Patent Office: 998 Days
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G01C 21/3658 Lane guidance
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/065 by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

Abstract

A plurality of instances of probe data are received. Each instance is matched to a link of a digital map. A distance parameter for each instance is determined based on the link. Based on a vehicle lane pattern for the link, a most likely lane for each instance is determined based on the corresponding distance parameter. Probe trajectories are constructed based on sequences of instances of probe data identified in the plurality of instances of probe data. At least one of the probe trajectories is analyzed based at least in part on lane change probabilities to generate at least one lane level trajectory. Lane level traffic information is determined based on the at least one lane level trajectory and the corresponding instances of probe data.

Claims

1. A method comprising:

receiving a plurality of instances of probe data, each instance being from a probe apparatus of a plurality of probe apparatuses, the probe apparatus comprising a plurality of sensors and being onboard a vehicle, wherein an instance of the plurality of instances of probe data comprises location information indicating a location of the corresponding probe apparatus;

for each of one or more instances of the plurality of instances of probe data, determining a distance parameter based on the location information and a road segment corresponding to the location;

based on a vehicle lane pattern for the road segment, identifying a most likely lane for each instance of the plurality of instances based on the corresponding distance parameter;

constructing one or more probe trajectories based on one or more sequences of instances of probe data identified in the plurality of instances of probe data;

analyzing at least one of the one or more probe trajectories based at least in part on lane change probabilities to generate at least one lane level trajectory;

determining lane level traffic information based on the at least one lane level trajectory; and

providing a lane level traffic information notification comprising at least a portion of the lane level traffic information to at least one computing entity, the at least one computing entity configured to perform at least one navigation-related function based at least in part on the lane level traffic information.

2. A method according to claim 1, wherein:

each instance of probe data further comprises (a) a probe apparatus identifier configured to identify the probe apparatus and (b) a timestamp,

each instance of probe data in a sequence of instances of probe data comprises a common probe identifier, and

the sequence of instances of probe data is sequenced based on the time stamps of the instances of probe data in the sequence.

3. A method according to claim 1, wherein analyzing the at least one of the one or more probe trajectories comprises:

generating a hidden Markov model based on the at least one of the one or more probe trajectories and the lane change probabilities;

obtaining a Viterbi-path corresponding to the at least one of the one or more probe trajectories; and

defining a lane level trajectory based on the Viterbi-path.

4. A method according to claim 1, wherein the lane level traffic information comprises at least one of:

a lane specific representative travel speed,

a lane specific distribution description of travel speed,

a lane specific traffic volume measurement,

a lane specific alert,

lane specific traffic jam information, or

a current lane for a particular vehicle.

5. A method according to claim 1, wherein performing the navigation-related function comprises (a) performing one or more route planning determinations, (b) providing a lane specific alert, or (c) both.

6. A method according to claim 1, wherein a sequence of instances of probe data comprises at least three instances of probe data.

7. A method according to claim 1, wherein the most likely lane for an instance of probe data is determined based at least in part on representative distance parameters of the vehicle lane pattern and the distance parameter on the instance of probe data.

8. A method according to claim 1, the distance parameter is determined by:

identifying a position on a link of a digital map corresponding to the road segment based on the location information;

determining a distance between the position and the location; and

assigning the distance parameter the value of the determined distance.

9. A method according to claim 8, wherein the position is located on a reference line corresponding to the road segment and the distance is equal to the length of a line extending from the position to the location, wherein the line is perpendicular to the reference line.

10. A method according to claim 1, wherein the lane change probabilities are determined based on historical probe data or apriori data.

11. An apparatus comprising at least one processor, at least one memory storing computer program code, with the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:

receive a plurality of instances of probe data, each instance being from a probe apparatus of a plurality of probe apparatuses, the probe apparatus comprising a plurality of sensors and being onboard a vehicle, wherein an instance of the plurality of instances of probe data comprises location information indicating a location of the corresponding probe apparatus;

for each of one or more instances of the plurality of instances of probe data, determine a distance parameter based on the location information and a road segment corresponding to the location;

based on a vehicle lane pattern for the road segment, identify a most likely lane for each instance of the plurality of instances based on the corresponding distance parameter;

construct one or more probe trajectories based on one or more sequences of instances of probe data identified in the plurality of instances of probe data;

analyze at least one of the one or more probe trajectories based at least in part on lane change probabilities to generate at least one lane level trajectory;

determine lane level traffic information based on the at least one lane level trajectory; and

provide a lane level traffic information notification comprising at least a portion of the lane level traffic information to at least one computing entity, the at least one computing entity configured to perform at least one navigation-related function based at least in part on the lane level traffic information.

12. An apparatus according to claim 11, wherein:

each instance of probe data further comprises (a) a probe apparatus identifier configured to identify the probe apparatus and (b) a timestamp,

each instance of probe data in a sequence of instances of probe data comprises a common probe identifier, and

the sequence of instances of probe data is sequenced based on the time stamps of the instances of probe data in the sequence.

13. An apparatus according to claim 11, wherein to analyze the at least one of the one or more probe trajectories the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:

generate a hidden Markov model based on the at least one of the one or more probe trajectories and the lane change probabilities;

obtain a Viterbi-path corresponding to the at least one of the one or more probe trajectories; and

define a lane level trajectory based on the Viterbi-path.

14. An apparatus according to claim 11, wherein the lane level traffic information comprises at least one of:

a lane specific representative travel speed,

a lane specific distribution description of travel speed,

a lane specific traffic volume measurement,

a lane specific alert,

lane specific traffic jam information, or

a current lane for a particular vehicle.

15. An apparatus according to claim 11, wherein performing the navigation function comprises (a) performing one or more route planning determinations, (b) providing a lane specific alert, or (c) both.

16. An apparatus according to claim 11, wherein a sequence of instances of probe data comprises at least three instances of probe data.

17. An apparatus according to claim 11, wherein the most likely lane for an instance of probe data is determined based at least in part on representative distance parameters of the vehicle lane pattern and the distance parameter on the instance of probe data.

18. An apparatus according to claim 11, wherein to determine the distance parameter the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:

identify a position on a link of a digital map corresponding to the road segment based on the location information;

determine a distance between the position and the location; and

assign the distance parameter the value of the determined distance.

19. An apparatus according to claim 18, wherein the position is located on a reference line corresponding to the road segment and the distance is equal to the length of a line extending from the position to the location, wherein the line is perpendicular to the reference line.

20. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein with the computer-executable program code instructions comprising program code instructions configured to:

receive a plurality of instances of probe data, each instance being from a probe apparatus of a plurality of probe apparatuses, the probe apparatus comprising a plurality of sensors and being onboard a vehicle, wherein an instance of the plurality of instances of probe data comprises location information indicating a location of the corresponding probe apparatus;

for each of one or more instances of the plurality of instances of probe data, determine a distance parameter based on the location information and a road segment corresponding to the location;

based on a vehicle lane pattern for the road segment, identify a most likely lane for each instance of the plurality of instances based on the corresponding distance parameter;

construct one or more probe trajectories based on one or more sequences of instances of probe data identified in the plurality of instances of probe data;

analyze at least one of the one or more probe trajectories based at least in part on lane change probabilities to generate at least one lane level trajectory;

determine lane level traffic information based on the at least one lane level trajectory; and

provide a lane level traffic information notification comprising at least a portion of the lane level traffic information to at least one computing entity, the at least one computing entity configured to perform at least one navigation-related function based at least in part on the lane level traffic information.