Lane-level geometry and traffic information

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.