Three-dimensional motion grid system for autonomous vehicle perception

Patent metadata

Field	Value
Publication number	US-2023410423-A1
Application number	US-202217841439-A
Country	US
Kind code	A1
Filing date	Jun 15, 2022
Priority date	Jun 15, 2022
Publication date	Dec 21, 2023
Grant date	—

Abstract

A method is described and includes receiving a frame of point cloud data from at least one onboard light detection and ranging (LIDAR) sensor of a vehicle; discarding points of the received frame of point cloud data that are outside a defined geographic area around the vehicle; and, subsequent to the discarding, performing ray tracing in connection with the remaining points of the received frame of point cloud data. The method may further include characterizing an occupancy condition of each of a plurality of cells of a three-dimensional (3D) grid corresponding to the defined geographic area based on the ray tracing of the received frame of point cloud data, wherein the 3D grid corresponds to the received frame of point cloud data.

First claim

What is claimed is:

1 . A method comprising:
receiving a frame of point cloud data from at least one onboard light detection and ranging (LIDAR) sensor of a vehicle; discarding points of the received frame of point cloud data that are outside a defined geographic area around the vehicle; subsequent to the discarding, performing ray tracing in connection with the remaining points of the received frame of point cloud data; characterizing an occupancy condition of each of a plurality of cells of a three-dimensional (3D) grid corresponding to the defined geographic area based on the ray tracing of the received frame of point cloud data, wherein the 3D grid corresponds to the received frame of point cloud data.

2 . The method of claim 1 , further comprising comparing the 3D grid to a 3D grid corresponding to a previous frame of point cloud data to determine motion of an object with the defined geographic area.

3 . The method of claim 1 , further comprising providing the 3D grid to a tracking system for the vehicle.

4 . The method of claim 1 , wherein the cell comprises a 3D cell and each column of the 3D grid comprises a plurality of 3D cells.

5 . The method of claim 1 , wherein the occupancy condition of one of the cells comprises at least one of occupied and freespace.

6 . The method of claim 5 , wherein the occupancy condition of one of the cells comprises at least one of overhang and ground.

7 . The method of claim 1 , wherein the motion comprises a direction of movement.

8 . The method of claim 1 , wherein the motion comprises a speed of movement.

9 . The method of claim 1 , wherein the ray tracing and characterizing is performed using a graphics processing unit (GPU).

10 . The method of claim 9 , wherein the ray tracing is performed using parallel processing.

11 . The method of claim 1 , further comprising converting the 3D grid into a two-dimensional (2D) occupancy grid, wherein each cell of the 2D occupancy grid comprises a consolidation of the cells of a corresponding column of the 3D grid.

12 . The method of claim 11 , further comprising comparing the 2D occupancy grid with an immediately preceding 2D occupancy grid to detect motion of an object over time within a geographic area represented by the 2D occupancy grid.

13 . The method of claim 1 , wherein the discarding points of the received frame of point cloud data that are outside the defined geographic area comprises discarding points that are indicated as corresponding to a point in space located above a maximum height above a ground level.

14 . A method comprising:
generating for a first frame of a light detection and ranging (LIDAR) point cloud for a vehicle a first grid corresponding to a geographic area around the vehicle, the first grid including a first plurality of cells each having associated therewith an occupancy condition; generating for a second frame of the LIDAR point cloud a second grid representing the geographic area around the vehicle, the second grid including a second plurality of cells each having associated therewith an occupancy condition; and performing a cell-by-cell comparison of the first and second grids to infer motion from relative occupancy conditions of the first plurality of cells and the second plurality of cells.

15 . The method of claim 14 , wherein the first and second grids comprise three-dimensional (3D) grids and the geographic area has a maximum height above a ground level.

16 . The method of claim 14 , wherein the first and second grids comprise two dimensional (2D) grids.

17 . The method of claim 14 , wherein the occupancy condition comprises at least one of an occupied condition, a freespace condition, an occluded condition, a ground condition and an overhang condition.

18 . A control system for a vehicle, the control system comprising:
a plurality of onboard light ranging and detection (LIDAR) sensors for generating LIDAR data comprising a frame of a point cloud related to an environment of the vehicle; and a motion grid module comprising a graphics processing unit (GPU) configured to:
discard points of the point cloud frame that fall outside a defined geographic area around the vehicle;
subsequent to the discarding, process the remaining points using ray tracing to characterize an occupancy condition of each of a plurality of cells of a three-dimensional (3D) grid corresponding to the defined geographic area, wherein the 3D grid corresponds to the point cloud data frame; and
convert the 3D grid to a two-dimensional (2D) grid, wherein each of a plurality of cells of the 2D grid represents a collective occupancy condition of a portion of the plurality of cells comprising a corresponding column of the 3D grid.

19 . The control system of claim 18 , further comprising providing at least one of the 3D grid and the 2D grid to a tracking system for the vehicle.

20 . The control system of claim 18 , wherein the occupancy condition comprises at least one of an occupied condition, a freespace condition, an occluded condition, an overhang condition, and a ground condition.

Assignees

• Gm Cruise Holdings Llc

Inventors

• Fasola Juan
• Kushwaha Shreyans
• Lo Eugene
• Zhou Xiaoyu

Classifications

• G06T17/05 Primary

  Geographic models · CPC title

• G06T15/06

  Ray-tracing · CPC title

• G06T7/20 Primary

  Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) · CPC title

• G01S17/89

  for mapping or imaging · CPC title

• G01S17/931

  of land vehicles · CPC title