Reversible lane active direction detection based on GNSS probe data

• US 10,446,022 B2
• Filed: 06/09/2017
• Issued: 10/15/2019
• Est. Priority Date: 06/09/2017
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: Fowe, James, Pellolio, Filippo, Radomy, Sam
• Primary Examiner(s): Smith, Isaac G
• Application Number:
  US15/618,482
• Publication Number:
  US 20180357890A1
• Time in Patent Office: 858 Days
• Field of Search: 701117, 701118
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/056 with provision for distinguishing direction of travel

Abstract

In an example embodiment, a plurality of sequences of instances of probe data are received. Each sequence of instances of probe data is captured and provided by a probe apparatus comprising a plurality of sensors and is onboard a vehicle. An instance of probe data comprises location information indicating a location of the corresponding probe apparatus and the instances are ordered by capture time to form the sequence of instances. A travel direction of each probe apparatus is determined based on the corresponding sequence. Each probe apparatus is matched to a lane of a road segment based on the determined travel direction and a predetermined vehicle lane pattern. The vehicle lane pattern comprises at least one reversible lane. Probe apparatuses matched to the at least one reversible lane are identified. An active direction is determined based on the number of identified probe apparatuses corresponding to each travel direction.

Claims

1. A method comprising:

receiving a plurality of sequences of instances of probe data by an apparatus comprising a processor and a communication interface, each sequence of instances of probe data being captured and provided by a probe apparatus of a plurality of probe apparatuses, the probe apparatus comprising a plurality of sensors and being onboard a vehicle, wherein (a) an instance of probe data (i) comprises location information indicating a location of the corresponding probe apparatus, the location information determined by a sensor onboard the vehicle and (ii) corresponds to a capture time at which the location information was captured and (b) a sequence of instances of probe data are ordered by the capture time corresponding to each instance of probe data;

determining, by the apparatus, a travel direction of each probe apparatus of the plurality of probe apparatuses based on the corresponding sequence of instances of probe data;

matching, by the apparatus, each probe apparatus to a lane of a road segment based on the determined travel direction and a predetermined vehicle lane pattern, the vehicle lane pattern comprising at least one reversible lane;

identifying, by the apparatus, a first number of probe apparatuses and a second number of probe apparatuses, wherein (a) the first number of probe apparatuses were matched to the reversible lane and have a travel direction of a first direction and (b) the second number of probe apparatuses were matched to the reversible lane and have a travel direction of a second direction;

based on the first number and the second number, determining, by the apparatus, an active direction for the at least one reversible lane, wherein determining the active direction for the at least one reversible lane comprises;

generating a hidden Markov model based on the first number and the second number for one or more epochs, wherein an epoch is a time window of a predetermined length, and

determining a probability that the active direction of the at least one reversible lane is one of each of a predefined set of states, the predefined set of states comprising;

a first direction, a second direction, and closed; and

providing the active direction such that a computing entity receives the active direction, wherein the computing entity is configured to perform a route planning determination based at least in part on the active direction.

2. The method according to claim 1, further comprising determining reversible lane traffic data corresponding to traffic conditions currently being experienced on the at least one reversible lane based on the sequences of instances of probe data corresponding to the probe apparatuses matched to the at least one reversible lane in the active direction.

3. The method according to claim 2, further comprising providing a reversible lane traffic data communication comprising at least a portion of the reversible lane traffic data to a computing entity, wherein, when the traffic data communication is processed by the computing entity, the traffic data communication causes the computing entity to (a) perform one or more route planning determinations, (b) provide an alert corresponding to the traffic conditions, or (c) both.

4. The method according to claim 1, further comprising determining a confidence metric for the active direction based on the number of probe apparatuses matched to the at least one reversible lane for each direction for one or more epochs, wherein an epoch is a time window of a predetermined length.

5. The method according to claim 4, wherein the active direction is determined in response to determining that the confidence metric satisfies a threshold requirement.

6. The method according to claim 1, wherein determining the active direction comprises selecting a state of the predefined set of states having the highest probability.

7. The method according to claim 6, wherein a confidence metric is determined for the active direction based at least in part on at least one previous active direction corresponding to at least one of the one or more epochs.

8. The method according to claim 1, wherein one or more transition probabilities of the hidden Markov model are time dependent.

9. The method according to claim 1, wherein one or more transition probabilities of the hidden Markov model for at least one of the one or more epochs is determined based on a schedule for the at least one reversible lane.

10. An apparatus comprising at least one processor, and 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 sequences of instances of probe data, each sequence of instances of probe data being captured and provided by a probe apparatus of a plurality of probe apparatuses, the probe apparatus comprising a plurality of sensors and being onboard a vehicle, wherein (a) an instance of probe data (i) comprises location information indicating a location of the corresponding probe apparatus and (ii) corresponds to a capture time at which the location information was captured and (b) a sequence of instances of probe data are ordered by the capture time corresponding to each instance of probe data;

determine a travel direction of each probe apparatus of the plurality of probe apparatuses based on the corresponding sequence of instances of probe data;

match each probe apparatus to a lane of a road segment based on the determined travel direction and a predetermined vehicle lane pattern, the vehicle lane pattern comprising at least one reversible lane;

identify a first number of probe apparatuses and a second number of probe apparatuses, wherein (a) the first number of probe apparatuses were matched to the reversible lane and have a travel direction of a first direction and (b) the second number of probe apparatuses were matched to the reversible lane and have a travel direction of a second direction; and

based on the first number and the second number, determine an active direction for the at least one reversible lane, wherein determining the active direction for the at least one reversible lane comprises;

generating a hidden Markov model based on the first number and the second number for one or more epochs, wherein an epoch is a time window of a predetermined length, and

determining a probability that the active direction of the at least one reversible lane is one of each of a predefined set of states, the predefined set of states comprising;

a first direction, a second direction, and closed; and

provide the active direction such that a computing entity receives the active direction, wherein the computing entity is configured to perform a route planning determination based at least in part on the active direction.

11. The apparatus according to claim 10, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least determine reversible lane traffic data corresponding to traffic conditions currently being experienced on the at least one reversible lane based on the sequences of instances of probe data corresponding to the probe apparatuses matched to the at least one reversible lane in the active direction.

12. The apparatus according to claim 11, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least provide a reversible lane traffic data communication comprising at least a portion of the reversible lane traffic data to a computing entity, wherein, when the traffic data communication is processed by the computing entity, the traffic data communication causes the computing entity to (a) perform one or more route planning determinations, (b) provide an alert corresponding to the traffic conditions, or (c) both.

13. The apparatus according to claim 10, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least determine a confidence metric for the active direction based on the number of probe apparatuses matched to the at least one reversible lane for each direction for one or more epochs, wherein an epoch is a time window of a predetermined length, wherein the active direction is determined in response to determining that the confidence metric satisfies a threshold requirement.

14. The apparatus according to claim 10, wherein determining the active direction comprises selecting a state of the predefined set of states having the highest probability.

15. The apparatus according to claim 14, wherein a confidence metric is determined for the active direction based at least in part on at least one previous active direction corresponding to at least one of the one or more epochs.

16. The apparatus according to claim 10, wherein one or more transition probabilities of the hidden Markov model are time dependent.

17. The apparatus according to claim 10, wherein one or more transition probabilities of the hidden Markov model for at least one of the one or more epochs is determined based on a schedule for the at least one reversible lane.

18. 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 sequences of instances of probe data, each sequence of instances of probe data being captured and provided by a probe apparatus of a plurality of probe apparatuses, the probe apparatus comprising a plurality of sensors and being onboard a vehicle, wherein (a) an instance of probe data (i) comprises location information indicating a location of the corresponding probe apparatus and (ii) corresponds to a capture time at which the location information was captured and (b) a sequence of instances of probe data are ordered by the capture time corresponding to each instance of probe data;

determine a travel direction of each probe apparatus of the plurality of probe apparatuses based on the corresponding sequence of instances of probe data;

match each probe apparatus to a lane of a road segment based on the determined travel direction and a predetermined vehicle lane pattern, the vehicle lane pattern comprising at least one reversible lane;

identify a first number of probe apparatuses and a second number of probe apparatuses, wherein (a) the first number of probe apparatuses were matched to the reversible lane and have a travel direction of a first direction and (b) the second number of probe apparatuses were matched to the reversible lane and have a travel direction of a second direction; and

based on the first number and the second number, determine an active direction for the at least one reversible lane, wherein determining the active direction for the at least one reversible lane comprises;

generating a hidden Markov model based on the first number and the second number for one or more epochs, wherein an epoch is a time window of a predetermined length, and

determining a probability that the active direction of the at least one reversible lane is one of each of a predefined set of states, the predefined set of states comprising;

a first direction, a second direction, and closed; and

provide the active direction such that a computing entity receives the active direction, wherein the computing entity is configured to perform a route planning determination based at least in part on the active direction.