LANE-LEVEL GEOMETRY AND TRAFFIC INFORMATION

• US 20190325738A1
• Filed: 04/18/2018
• Published: 10/24/2019
• Est. Priority Date: 04/18/2018
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: DORUM, Ole Henry
• Granted Patent: US 10,482,761 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/3658 Lane guidance
• CPC Class Codes: G01S 19/51 Relative positioning
• CPC Class Codes: G05D 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 17/15 Correlation function computation including computation of convolution operations arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• 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

Abstract

Provided herein is a method for establishing lane-level data from probe data. Methods may include receiving probe data points associated with a plurality of vehicles; determining, for each of the probe data points, a location and road segment corresponding to the location; generating, from the probe data points associated with a first road segment, a probe density histogram for the first road segment, where the probe density histogram represents a volume of probe data points at each of a plurality of positions across a width of the first road segment; applying a deconvolution method to the probe density histogram to obtain a multi-modal histogram; determining, from the multi-modal histogram, a number of statistically significant peaks, where each statistically significant peak represents a lane of the first road segment; and computing, from the multi-modal histogram, lane-level properties of the probe data of the first road segment.

Claims

1. A mapping system comprising:

a memory comprising map data; and

processing circuitry configured to;

receive probe data points associated with a plurality of vehicles, each probe data point received from a probe apparatus of a plurality of probe apparatuses, each probe apparatus comprising one or more sensors and being onboard a respective vehicle, wherein each probe data point comprises location information associated with the respective probe apparatus;

for each of the probe data points, determine a location and a road segment corresponding to the location;

generate, from the probe data points associated with a first road segment, a probe density histogram for the first road segment, wherein the probe density histogram represents a volume of probe data points at each of a plurality of positions across a width of the first road segment;

apply a deconvolution method to the probe density histogram to obtain a multi-modal histogram;

determine, from the multi-modal histogram, a number of statistically significant peaks, wherein each statistically significant peak represents a lane of the first road segment;

compute, from the multi-modal histogram, lane-level properties of the probe data of the first road segment; and

provide data for at least one of navigational assistance or at least semi-autonomous vehicle control based on the computed lane-level properties of the probe data of the first road segment.

2. The mapping system of claim 1, wherein the processing circuitry configured to compute, from the multi-modal histogram, lane-level properties of the probe data of the first road segment comprises processing circuitry configured to:

determine, from the multi-modal histogram, a distance of each statistically significant peak from the first road segment centerline.

3. The mapping system of claim 2, wherein the processing circuitry configured to compute, from the multi-modal histogram, lane-level properties of the probe data of the first road segment comprises processing circuitry further configured to:

generate digital map data having a number of road segment lanes corresponding to the number of statistically significant peaks at positions corresponding to the distance of each statistically significant peak from the road segment centerline; and

provide for at least semi-autonomous vehicle control or navigation assistance using the generated digital map data.

4. The mapping system of claim 1, wherein the processing circuitry configured to generate a probe density histogram for the first road segment representing a volume of probe data points at each of a plurality of positions across a width of the road segment comprises processing circuitry configured to:

sub-divide a width of the first road segment into a plurality of bins according to a chosen bin size;

bin each probe data point to a respective one of the plurality of bins corresponding to a distance of the respective probe data point from a centerline of the road segment; and

generate the probe density histogram based on a volume of probe data points in each bin across a width of the road segment.

5. The mapping system of claim 1, wherein the deconvolution method comprises a Maximum Entropy Method.

6. The mapping system of claim 1, wherein the processing circuitry configured to apply a deconvolution method to the probe density histogram to obtain a multi-modal histogram comprises processing circuitry configured to:

model location error of the probe data points associated with the first road segment using a point spread function;

apply the deconvolution method to the probe density histogram using the point spread function; and

generate the multi-modal histogram for the first road segment.

7. The mapping system of claim 1, wherein the processing circuitry configured to determine, from the multi-modal histogram, a number of statistically significant peaks, wherein each statistically significant peak represents a lane of the first road segment;

comprises processing circuitry configured to;

identify a lane of the first road segment in response to a corresponding statistically significant peak being within a range of 2.5 meters to 3.6 meters of another statistically significant peak.

8. The mapping system of claim 1, wherein the processing circuitry configured to, for each of the probe data points, determine a location and a road segment corresponding to the location comprises processing circuitry configured to:

map-match the probe data points to the first road segment;

sub-divide the road segment into a plurality of sub-segments; and

associate each of the probe data points map-matched to the first road segment to one of the plurality of sub-segments, wherein the processing circuitry configured to generate, from the probe data points associated with a first road segment, a probe density histogram for the first road segment comprises processing circuitry configured to;

generate, from the probe data points associated with a first road segment, a probe density histogram for each of the sub-segments of the first road segment.

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

receive probe data points associated with a plurality of vehicles, each probe data point received from a probe apparatus of a plurality of probe apparatuses, each probe apparatus comprising one or more sensors and being onboard a respective vehicle, wherein each probe data point comprises location information associated with the respective probe apparatus;

for each of the probe data points, determine a location and a road segment corresponding to the location;

generate, from the probe data points associated with a first road segment, a probe density histogram for the first road segment, wherein the probe density histogram represents a volume of probe data points at each of a plurality of positions across a width of the first road segment;

apply a deconvolution method to the probe density histogram to obtain a multi-modal histogram;

determine, from the multi-modal histogram, a number of statistically significant peaks, wherein each statistically significant peak represents a lane of the first road segment;

compute, from the multi-modal histogram, lane-level properties of the probe data of the first road segment; and

store the computed lane-level properties of the probe data of the first road segment to augment a geographic database.

10. The computer program product of claim 9, wherein the program code instructions to compute, from the multi-modal histogram, lane-level properties of the probe data of the first road segment comprises program code instructions to:

determine, from the multi-modal histogram, a distance of each statistically significant peak from the first road segment centerline.

11. The computer program product of claim 10, wherein the program code instructions to compute, from the multi-modal histogram, lane-level properties of the probe data of the first road segment comprises program code instructions to:

generate digital map data having a number of road segment lanes corresponding to the number of statistically significant peaks at positions corresponding to the distance of each statistically significant peak from the road segment centerline; and

provide for at least semi-autonomous vehicle control or navigation assistance using the generated digital map data.

12. The computer program product of claim 9, wherein the program code instructions to generate a probe density histogram for the first road segment representing a volume of probe data points at each of a plurality of positions across a width of the road segment comprises program code instructions to:

sub-divide a width of the first road segment into a plurality of bins according to a chosen bin size;

bin each probe data point to a respective one of the plurality of bins corresponding to a distance of the respective probe data point from a centerline of the road segment; and

generate the probe density histogram based on a volume of probe data points in each bin across a width of the road segment.

13. The computer program product of claim 9, wherein the deconvolution method comprises a Maximum Entropy Method.

14. The computer program product of claim 9, wherein the program code instructions to apply a deconvolution method to the probe density histogram to obtain a multi-modal histogram comprises program code instructions to:

model location error of the probe data points associated with the first road segment point spread function using a point spread function;

apply the deconvolution method to the probe density histogram using the point spread function; and

generate the multi-modal histogram for the first road segment.

15. The computer program product of claim 9, wherein the program code instructions to determine, from the multi-modal histogram, a number of statistically significant peaks, wherein each statistically significant peak represents a lane of the first road segment;

comprises program code instructions to;

identify a lane of the first road segment in response to a corresponding statistically significant peak being within a range of 2.5 meters to 3.6 meters of another statistically significant peak.

16. The computer program product of claim 9, wherein the program code instructions to, for each of the probe data points, determine a location and a road segment corresponding to the location comprises program code instructions to:

map-match the probe data points to the first road segment;

sub-divide the road segment into a plurality of sub-segments; and

associate each of the probe data points map-matched to the first road segment to one of the plurality of sub-segments, wherein the program code instructions to generate, from the probe data points associated with a first road segment, a probe density histogram for the first road segment comprises causing the apparatus to;

generate, from the probe data points associated with a first road segment, a probe density histogram for each of the sub-segments of the first road segment.

17. A method for establishing lane-level data from probe data comprising:

receiving probe data points associated with a plurality of vehicles, each probe data point received from a probe apparatus of a plurality of probe apparatuses, each probe apparatus comprising one or more sensors and being onboard a respective vehicle, wherein each probe data point comprises location information associated with the respective probe apparatus;

for each of the probe data points, determining a location and a road segment corresponding to the location;

generating, from the probe data points associated with a first road segment, a probe density histogram for the first road segment, wherein the probe density histogram represents a volume of probe data points at each of a plurality of positions across a width of the first road segment;

applying a deconvolution method to the probe density histogram to obtain a multi-modal histogram;

determining, from the multi-modal histogram, a number of statistically significant peaks, wherein each statistically significant peak represents a lane of the first road segment;

computing, from the multi-modal histogram, lane-level properties of the probe data of the first road segment; and

providing data for at least one of navigational assistance or at least semi-autonomous vehicle control based on the computed lane-level properties of the probe data of the first road segment.

18. The method of claim 17, wherein computing, from the multi-modal histogram, lane-level properties of the probe data of the first road segment comprises:

determining, from the multi-modal histogram, a distance of each statistically significant peak from the first road segment centerline.

19. The method of claim 18, wherein computing, from the multi-modal histogram, lane-level properties of the probe data of the first road segment comprises:

generating digital map data having a number of road segment lanes corresponding to the number of statistically significant peaks at positions corresponding to the distance of each statistically significant peak from the road segment centerline; and

providing for at least semi-autonomous vehicle control or navigation assistance using the generated digital map data.

20. The method of claim 17, wherein generating a probe density histogram for the first road segment representing a volume of probe data points at each of a plurality of positions across a width of the road segment comprises:

sub-dividing a width of the first road segment into a plurality of bins according to a chosen bin size;

binning each probe data point to a respective one of the plurality of bins corresponding to a distance of the respective probe data point from a centerline of the road segment; and

generating the probe density histogram based on a volume of probe data points in each bin across a width of the road segment.