US 10,380,890 B2 · Autonomous vehicle localization based on walsh kernel projection technique

Abstract

Location of an autonomous driving vehicle (ADV) is determined with respect to a high definition map. On-boards sensors of the ADV obtain a 3D point cloud of objects surrounding the ADV. The 3D point cloud is organized into an ADV feature space of cells. Each cell has a median intensity value and a variance in elevation. A set of candidate cells that surround the ADV is determined. For each candidate, a set of cells of the ADV feature space that surround the candidate cell is projected onto the map feature space using kernel projection, for one or more dimensions. Kernels can be Walsh-Hadamard vectors. Candidates having insufficient similarity are rejected. When a threshold number of non-rejected candidates remain, candidate similarity can be determined using a similarity metric. The coordinates of the most similar candidate cell are used to determine the position of the vehicle with respect to the map.

Claims21

Dependent claims

A computer-implemented method of determining a location of an autonomous driving vehicle (ADV) with respect to a high definition (HD) map and navigating the ADV, the method comprising:

determining a first sub-set of a plurality of candidate cells of an ADV feature space of cells surrounding the ADV, the ADV feature space derived from a three-dimensional (3D) point cloud of sensor data obtained by sensors of the ADV;
for each candidate cell in the first sub-set of the plurality of candidate cells:
determining a similarity score between a subset of the ADV feature space that surrounds the candidate cell and an HD map feature space, by projecting the subset of the ADV feature space onto the map feature space using a first dimension projection kernel;
in response to determining that the similarity score is less than a threshold amount, marking the candidate cell as rejected, otherwise storing the similarity score in association with the candidate cell;
determining a location of the ADV with respect to the HD map feature space based at least in part on a candidate cell in the plurality of candidate cells having a highest similarity score among the plurality of candidate cells;
and
navigating the ADV along a planned route, the navigating based at least in part on the determined location of the ADV with respect to the HD map.

4 more elements

A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for determining a location of an autonomous driving vehicle (ADV) with respect to a high definition (HD) map, the operations comprising:

determining a first sub-set of a plurality of candidate cells of an ADV feature space of cells surrounding the ADV, the ADV feature space derived from a three-dimensional (3D) point cloud of sensor data obtained by sensors of the ADV;
for each candidate cell in the first sub-set of the plurality of candidate cells:
determining a similarity score between a subset of the ADV feature space that surrounds the candidate cell, and an HD map feature space, by projecting the subset of the ADV feature space onto the map feature space using a first dimension projection kernel;
and
in response to determining that the similarity score is less than a threshold amount, marking the candidate cell as rejected, otherwise storing the similarity score in association with the candidate cell;
determining a location of the ADV with respect to the HD map feature space based at least in part on a candidate cell in the plurality of candidate cells having a highest similarity score among the plurality of candidate cells;
and
navigating the ADV along a planned route, the navigating based at least in part on the determined location of the ADV with respect to the HD map.

5 more elements

A data processing system, comprising:

a processor;
and
a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for determining a location of an autonomous driving vehicle (ADV) with respect to a high definition (HD) map and navigating the ADV, the operations including:
determining a first sub-set of a plurality of candidate cells of an ADV feature space of cells surrounding the ADV, the ADV feature space derived from a three-dimensional (3D) point cloud of sensor data obtained by sensors of the ADV;
for each candidate cell in the first sub-set of the plurality of candidate cells:
determining a similarity score between a subset of the ADV feature space that surrounds the candidate cell, and an HD map feature space, by projecting the subset of the ADV feature space onto the map feature space using a first dimension projection kernel;
and
in response to determining that the similarity score is less than a threshold amount, marking the candidate cell as rejected, otherwise storing the similarity score in association with the candidate cell;
determining a location of the ADV with respect to the HD map feature space based at least in part on a candidate cell in the plurality of candidate cells having a highest similarity score among the plurality of candidate cells;
and
navigating the ADV along a planned route, the navigating based at least in part on the determined location of the ADV with respect to the HD map.

8 more elements

Dependencies are inferred from the claim text.

Autonomous vehicle localization based on walsh kernel projection technique

Life of this patent

Autonomous vehicle localization based on walsh kernel projection technique

Life of this patent

Abstract

Claims21