ROAD WALL SHAPE ESTIMATION DEVICE AND ROAD WALL SHAPE ESTIMATION METHOD

Abstract

Provided is a road wall shape estimation device for estimating the shape of a road wall at a long distance from a vehicle, with high accuracy. This road wall shape estimation device includes: a filter processing unit which sets a plurality of filters, and among observation points acquired by an observation data acquisition unit, outputs only observation points whose relative-to-ground velocities are equal to or smaller than a predetermined value and whose positions are inside the filters; a reference point calculation unit which calculates reference points from the output of the filter processing unit; and a road wall estimation unit which estimates the shape of the road wall from the reference points. The filter processing unit increases the width of each filter as the position of the filter becomes farther from an own vehicle.

Description

Claims

1. A road wall shape estimation device to estimate a shape of a road wall from information of an observation point cloud acquired by surrounding environment sensing device provided to an own vehicle, the road wall shape estimation device comprising: an own-vehicle motion data acquisition circuitry to acquire motion information including a velocity of the own vehicle from own-vehicle motion sensing device;an observation data acquisition circuitry to acquire the information of the observation point cloud from the surrounding environment sensing device;a filter processing circuitry to set, in a virtual space where the observation point cloud is distributed, a plurality of rectangular filters in which a frontward direction of the own vehicle or an extending direction of a road wall candidate is a depth direction of each filter and a direction perpendicular to the depth direction is a width direction of each filter, such that the filters are arranged in the frontward direction of the own vehicle, and from the observation point cloud acquired by the observation data acquisition circuitry, outputs only observation points whose relative-to-ground velocities are equal to or smaller than a predetermined value and whose positions are inside the filters;a reference point calculation circuitry to calculate a reference point in each filter from the output of the filter processing; anda road wall estimation circuitry to estimate the shape of the road wall from the reference points, wherein the filter processing circuitry increases a width of each filter as a position of the filter becomes farther from the own vehicle.

2. The road wall shape estimation device according to claim 1, wherein the reference point calculation circuitry performs clustering processing of making a cluster from the observation points which are the output of the filter processing circuitry on the basis of similarities, and calculates the reference point from a result of the clustering processing.

3. The road wall shape estimation device according to claim 1, further comprising an observation data group accumulation circuitry to accumulate data of observation points selected by the filter processing circuitry, wherein the reference point calculation circuitry calculates the reference point from the output of the filter processing circuitry and information of the observation points accumulated in the observation data group accumulation circuitry.

4. The road wall shape estimation device according to claim 1, further comprising an observation data group accumulation circuitry to accumulate data of observation points selected by the filter processing circuitry, wherein the reference point calculation circuitry performs clustering processing of making a cluster from observation points including both of the output of the filter processing circuitry and the observation points accumulated in the observation data group accumulation circuitry, on the basis of similarities, and calculates the reference point from a result of the clustering processing.

5. A road wall shape estimation method to estimate a shape of a road wall from information of an observation point cloud acquired by surrounding environment sensing device provided to an own vehicle, the road wall shape estimation method comprising: an own-vehicle motion data acquisition acquiring motion information including a velocity of the own vehicle from own-vehicle motion sensing device;an observation data acquisition acquiring the information of the observation point cloud from the surrounding environment sensing device;a filter update setting, in a virtual space where the observation point cloud is distributed, a plurality of rectangular filters in which a frontward direction of the own vehicle or an extending direction of a road wall candidate is a depth direction of each filter and a direction perpendicular to the depth direction is a width direction of each filter, such that the filters are arranged in the frontward direction of the own vehicle, so as to increase the width of each filter as a position of the filter becomes farther from the own vehicle;a filter processing, from the observation point cloud acquired in the observation data acquisition, selecting only observation points whose relative-to-ground velocities are equal to or smaller than a predetermined value and whose positions are inside the filters;a reference point calculation calculating a reference point in each filter from the observation points selected in the filter processing; anda road wall estimation estimating the shape of the road wall from the reference points.

6. The road wall shape estimation method according to claim 5, wherein in the reference point calculation, clustering processing of making a cluster from the observation points selected in the filter processing on the basis of similarities is performed, and the reference point is calculated from a result of the clustering processing.

7. The road wall shape estimation method according to claim 5, further comprising an observation point accumulation accumulating data of observation points selected in the filter processing-step, wherein in the reference point calculation, the reference point is calculated from the observation points selected in the filter processing-step and the observation points accumulated in the observation point accumulation.

8. The road wall shape estimation method according to claim 5, further comprising an observation point accumulation accumulating data of observation points selected in the filter processing-step, wherein in the reference point calculation-step, clustering processing of making a cluster from observation points including both of the observation points selected in the filter processing and the observation points accumulated in the observation point accumulation, on the basis of similarities, is performed, and the reference point is calculated from a result of the clustering processing.

9. The road wall shape estimation method according to claim 5, wherein in the filter update-step, the plurality of filters are set adjacently to each other in the frontward direction of the own vehicle such that the frontward direction of the own vehicle is the depth direction of each filter and center positions in the width direction of the respective filters are aligned straightly in the frontward direction of the own vehicle.

10. The road wall shape estimation method according to claim 5, further comprising a road wall candidate acquisition acquiring information of the road wall candidate, wherein in the filter update-step, the plurality of filters are set along the road wall candidate adjacently to each other in the frontward direction of the own vehicle such that the frontward direction of the own vehicle is the depth direction of each filter, and if reliability of the road wall candidate is lower than a predetermined value, a size of the filter is enlarged.

11. The road wall shape estimation method according to claim 5, further comprising a road wall candidate acquisition acquiring information of the road wall candidate, wherein in the filter update-step, the plurality of rectangular filters are set along the road wall candidate such that the extending direction of the road wall candidate is the depth direction of each filter, and if reliability of the road wall candidate is lower than a predetermined value, a size of the filter is enlarged.

12. The road wall shape estimation method according to claim 10, wherein the road wall candidate acquired in the road wall candidate acquisition-step is any of information of a road wall shape estimated at a past observation time, information acquired by second surrounding environment sensing device, or information acquired from map information.

13. The road wall shape estimation method according to claim 6, wherein in the clustering processing, observation points present in a high-density area where the observation points are densely present so as to be close to each other are extracted as the cluster that is a clustering result, and an observation point away from the cluster is excluded as an outlier.

14. The road wall shape estimation method according to claim 7, wherein in the filter update, a maximum accumulation period of observation point information is set for each filter such that the maximum accumulation period increases as the filter becomes farther from the own vehicle, andthe road wall shape estimation method further comprises an accumulated data update, among the observation points accumulated in the observation point accumulation step, deleting an observation point present outside each filter, and deleting an observation point which is present inside each filter and of which an accumulation period is greater than the maximum accumulation period set for the filter.

15. The road wall shape estimation method according to claim 5, wherein in the reference point calculation, when the reference point is calculated from estimation target observation points, a position of the reference point is any of an average position of positions of the estimation target observation points, a position of the estimation target observation point closest to the own vehicle, or a most frequent position of the estimation target observation points.