Learning signs from vehicle probes

• US 9,459,626 B2
• Filed: 12/11/2014
• Issued: 10/04/2016
• Est. Priority Date: 12/11/2014
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Chen, Xin, Ma, Di, Ma, Xiang, Ostrovskiy, Roman, Zhukov, Vlad, Zou, Xiaotao
• Primary Examiner(s): MARC-COLEMAN, MARTHE Y
• Application Number:
  US14/567,343
• Publication Number:
  US 20160170414A1
• Time in Patent Office: 663 Days
• Field of Search: 701/27, 701/57, 701/58, 701/97, 701/119, 707/999.102, 707/999.104, 707/E17.018
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06F 18/2453 non-linear, e.g. polynomial classifier
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/582 of traffic signs
• 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 1/09623 Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096758 where no selection takes place on the transmitted or the received information
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle

Abstract

Systems, methods, and apparatuses are disclosed for identifying road signs along a roadway segment from vehicle probe data. Probe data is received from vehicle sensors at a road segment, wherein the probe data includes observed static objects along the road segment. Road signs are identified within the observed static objects of the probe data using a logistic regression algorithm. The geographic location of the identified road signs are determined using a linear regression algorithm.

Claims

1. A method comprising:

receiving, using a processor, probe data from vehicle sensors from a plurality of vehicles at a road segment, the probe data comprising observed static objects along the road segment;

identifying, using a logistic regression algorithm, road signs within the observed static objects of the probe data; and

determining, using a linear regression algorithm, a geographic location for each identified road sign along the road segment.

2. The method of claim 1, wherein one of the road signs is a speed limit sign.

3. The method of claim 2, wherein the speed limit sign is a fixed speed limit sign.

4. The method of claim 2, wherein the speed limit sign is a variable speed limit sign.

5. The method of claim 1, further comprising:

ascertaining, for each road sign, whether each of the road signs is a fixed road sign or a variable road sign.

6. The method of claim 1, further comprising:

reporting the determined geographic location of the road signs over a connected network to a traffic developer, a map developer, or a navigation device.

7. The method of claim 1, further comprising:

reporting data comprising the determined geographic location of the identified road signs over a connected network to a navigation device within an autonomous vehicle or a highly automated driving vehicle;

processing the reported data within the autonomous vehicle or the highly automated driving vehicle; and

generating a driving command or alerting an operator of the autonomous vehicle or the highly automated driving vehicle based on the processed data.

8. The method of claim 1, further comprising:

training the logistic regression algorithm through a verification of the identified road signs with ground truth data.

9. The method of claim 1, further comprising:

training the linear regression algorithm through a verification of the determined geographic location of each identified road sign with ground truth data.

10. The method of claim 1, further comprising:

map-matching each road sign to an edge of the road segment or a specific lane of the road segment.

11. A method comprising:

receiving, using a processor, probe data from vehicle sensors from a plurality of vehicles at a road segment, the probe data comprising observed static objects along the road segment;

identifying, using a logistic regression algorithm, speed signs within the observed static objects of the probe data;

ascertaining, for each speed sign, whether the speed sign is a fixed speed sign or a variable speed sign;

determining, using a linear regression algorithm, a geographic location for each identified speed sign along the road segment; and

reporting the determined geographic location of the speed signs over a connected network to a traffic developer, a map developer, or a navigation device.

12. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs;

the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least perform;

receive probe data from vehicle sensors from a plurality of vehicles at a road segment, the probe data comprising observed static objects along the road segment;

identify, using a logistic regression algorithm, road signs within the observed static objects of the probe data; and

determine, using a linear regression algorithm, a geographic location for each identified road sign along the road segment.

13. The apparatus of claim 12, wherein one of the road signs is a speed limit sign.

14. The apparatus of claim 13, wherein the speed limit sign is a fixed speed limit sign.

15. The apparatus of claim 13, wherein the speed limit sign is a variable speed limit sign.

16. The apparatus of claim 12, wherein the at least one memory and the computer program code are configured to cause the apparatus to further perform:

ascertain, for each road sign, whether the road sign is a fixed road sign or a variable road sign.

17. The apparatus of claim 12, wherein the at least one memory and the computer program code are configured to cause the apparatus to further perform:

report the determined geographic location of the road signs over a connected network to a traffic developer, a map developer, or a navigation device.

18. The apparatus of claim 12, wherein the at least one memory and the computer program code are configured to cause the apparatus to further perform:

train the logistic regression algorithm through a verification of the identified road signs with ground truth data.

19. The apparatus of claim 12, wherein the at least one memory and the computer program code are configured to cause the apparatus to further perform:

train the linear regression algorithm through a verification of the determined geographic location of each identified road sign with ground truth data.

20. The apparatus of claim 12, wherein the at least one memory and the computer program code are configured to cause the apparatus to further perform:

map-match each road sign to an edge of the road segment or a specific lane of the road segment.