Learning lanes from radar data

• US 9,721,471 B2
• Filed: 12/16/2014
• Issued: 08/01/2017
• Est. Priority Date: 12/16/2014
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Ma, Di, Ma, Xiang, Ostrovskiy, Roman, Zhukov, Vladimir, Zou, Xiaotao, Chen, Xin
• Primary Examiner(s): OH, HARRY Y
• Application Number:
  US14/572,197
• Publication Number:
  US 20160171893A1
• Time in Patent Office: 959 Days
• Field of Search: 701300
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 15/931 of land vehicles
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/932 using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/9322 using additional data, e.g. driver condition, road state or weather data
• CPC Class Codes: G01S 2013/9323 Alternative operation using light waves
• CPC Class Codes: G01S 2013/9324 Alternative operation using ultrasonic waves
• 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 1/165 for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

Systems, methods, and apparatuses are disclosed for determining lane information of a roadway segment from vehicle probe data. Probe data is received from radar sensors of vehicles at a road segment, where the probe data includes an identification of static objects and dynamic objects in proximity to the respective vehicles at the road segment, and geographic locations of the static objects and the dynamic objects. A reference point, such as a road boundary, at the road segment is determined from the identified static objects. Lateral distances between the identified dynamic objects and the reference point are calculated. A number of lanes at the road segment are ascertained from a distribution of the calculated distances of the identified dynamic objects from the reference point.

Claims

1. A method comprising:

receiving probe data from radar sensors of vehicles at a road segment, the probe data comprising an identification of static objects and dynamic objects in proximity to the respective vehicles at the road segment, and geographic locations of the static objects and the dynamic objects;

determining, using a processor, a common reference point at the road segment among the identified static objects received from the vehicles;

calculating lateral distances between the identified dynamic objects and the common reference point using the geographic locations of the identified dynamic objects and the common reference point, each lateral distance measured along an axis laterally perpendicular to lanes of the road segment;

ascertaining a number of lanes at the road segment from a distribution of the calculated distances of the identified dynamic objects from the common reference point, wherein the distribution of the calculated distances of the identified dynamic objects is arranged in a histogram, and the number of lanes at the road segment are ascertained based on a number of peaks in the histogram; and

updating a map database with the ascertained number of lanes at the road segment.

2. The method of claim 1, further comprising:

defining each lane of the ascertained number of lanes at the road segment with a respective lane number; and

identifying the lane number of a vehicle based on a shortest lateral distance of the vehicle to a location represented by a peak in the histogram.

3. The method of claim 1, further comprising:

verifying the number of lanes at the road segment using ground truth data.

4. The method of claim 1, wherein the common reference point is a position at a road boundary.

5. The method of claim 1, wherein the dynamic objects identified are traveling in a same direction as the respective vehicles.

6. 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;

receive probe data from radar sensors of vehicles at a road segment, the probe data comprising an identification of static objects and dynamic objects in proximity to the respective vehicles at the road segment, and geographic locations of the static objects and the dynamic objects;

determine a common reference point at the road segment among the static objects received from the vehicles;

calculate lateral distances of the identified dynamic objects from the common reference point using the geographic locations of the identified dynamic objects and the common reference point, each lateral distance measured along an axis laterally perpendicular to lanes of the road segment;

ascertain a number of lanes at the road segment from a distribution of the calculated distances of the identified dynamic objects from the common reference point, wherein the distribution of the calculated distances of the identified dynamic objects is arranged in a histogram, and the number of lanes at the road segment are ascertained based on the number of peaks in the histogram; and

update a map database with the ascertained number of lanes at the road segment.

7. The apparatus of claim 6, wherein the common reference point is a position at a road boundary.

8. The apparatus of claim 6, wherein the at least one memory and the computer program code are configured to further cause the apparatus to:

define each lane of the ascertained number of lanes at the road segment with a respective lane number; and

identify the lane number of a vehicle based on a shortest distance of the vehicle to a location represented by a peak in the histogram.

9. A method comprising:

receiving probe data from radar sensors of vehicles at a road segment, the probe data comprising an identification of static objects and dynamic objects in proximity to the respective vehicles at the road segment, and geographic locations of the static objects and the dynamic objects;

determining, using a processor, a common reference point at the road segment among the identified static objects received from the vehicles;

calculating lateral distances between the identified dynamic objects and the common reference point using the geographic locations of the identified dynamic objects and the common reference point, each lateral distance measured along an axis laterally perpendicular to lanes of the road segment;

ascertaining a number of lanes at the road segment from a distribution of the calculated distances of the identified dynamic objects from the common reference point, wherein the distribution of the calculated distances of the identified dynamic objects is arranged in a histogram, and the number of lanes at the road segment are ascertained based on a number of peaks in the histogram; and

reporting the ascertained number of lanes at the road segment to a vehicle navigation device over a connected network.

10. The method of claim 9, further comprising:

defining each lane of the ascertained number of lanes at the road segment with a respective lane number; and

identifying the lane number of a vehicle based on a shortest lateral distance of the vehicle to a location represented by a peak in the histogram.

11. The method of claim 9, further comprising:

verifying the number of lanes at the road segment using ground truth data.

12. The method of claim 9, wherein the common reference point is a position at a road boundary.

13. The method of claim 9, wherein the dynamic objects identified are traveling in a same direction as the respective vehicles.

14. 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;

receive probe data from radar sensors of vehicles at a road segment, the probe data comprising an identification of static objects and dynamic objects in proximity to the respective vehicles at the road segment, and geographic locations of the static objects and the dynamic objects;

determine a common reference point at the road segment among the static objects received from the vehicles;

calculate lateral distances of the identified dynamic objects from the common reference point using the geographic locations of the identified dynamic objects and the common reference point, each lateral distance measured along an axis laterally perpendicular to lanes of the road segment;

ascertain a number of lanes at the road segment from a distribution of the calculated distances of the identified dynamic objects from the common reference point, wherein the distribution of the calculated distances of the identified dynamic objects is arranged in a histogram, and the number of lanes at the road segment are ascertained based on the number of peaks in the histogram; and

report the ascertained number of lanes at the road segment to a vehicle navigation device over a connected network.

15. The apparatus of claim 14, wherein the common reference point is a position at a road boundary.

16. The apparatus of claim 14, wherein the at least one memory and the computer program code are configured to further cause the apparatus to:

define each lane of the ascertained number of lanes at the road segment with a respective lane number; and

identify the lane number of a vehicle based on a shortest distance of the vehicle to a location represented by a peak in the histogram.