Systems and methods for integrating radar data for improved object detection in autonomous vehicles

Patent metadata

Field	Value
Publication number	US-12013457-B2
Application number	US-202117150590-A
Country	US
Kind code	B2
Filing date	Jan 15, 2021
Priority date	Mar 5, 2020
Publication date	Jun 18, 2024
Grant date	Jun 18, 2024

Abstract

Systems and methods for integrating radar and LIDAR data are disclosed. In particular, a computing system can access radar sensor data and LIDAR data for the area around the autonomous vehicle. The computing system can determine, using the one or more machine-learned models, one or more objects in the area of the autonomous vehicle. The computing system can, for a respective object, select a plurality of radar points from the radar sensor data. The computing system can generate a similarity score for each selected radar point. The computing system can generate weight associated with each radar point based on the similarity score. The computing system can calculate predicted velocity for the respective object based on a weighted average of a plurality of velocities associated with the plurality of radar points. The computing system can generate a proposed motion plan based on the predicted velocity for the respective object.

First claim

What is claimed is:

1. A computer-implemented method for generating perception and prediction data for autonomous vehicles, the method comprising:
accessing radar sensor data for an area around an autonomous vehicle, the radar sensor data including a plurality of radar points;
accessing LIDAR sensor data for the area around the autonomous vehicle;
generating, using one or more machine-learned models, a fused representation of the area around the autonomous vehicle based on the radar sensor data and the LIDAR sensor data;
determining, using the one or more machine-learned models, one or more objects in the area of the autonomous vehicle based on the fused representation;
for a respective object in the one or more objects:
determining a plurality of radar points from the radar sensor data that are associated with the respective object, each radar point having an associated velocity;
generating, using a machine-learned model, a similarity score for each determined radar point;
generating a weight associated with each radar point based, at least in part, on the similarity score; and
calculating a predicted velocity for the respective object based on a weighted average of the velocities of the plurality of radar points determined to be associated with the respective object;

generating a proposed motion plan based on the predicted velocity for the respective object; and
transmitting vehicle motion controls to one or more vehicle control systems to implement the motion plan.

2. The computer-implemented method of claim 1 , wherein the radar sensor data includes, for each radar point, a location of the radar point and a velocity associated with the radar point.

3. The computer-implemented method of claim 1 , wherein the radar sensor data includes data from a plurality of cycles of the radar sensor.

4. The computer-implemented method of claim 1 , wherein the LIDAR sensor data includes a plurality of LIDAR points, each LIDAR point having an associated location.

5. The computer-implemented method of claim 1 , wherein the LIDAR sensor data includes data from a plurality of sweeps of the LIDAR sensor.

6. The computer-implemented method of claim 1 , wherein generating, using one or more machine-learned models, a fused representation of the area around the autonomous vehicle based on the radar sensor data and the LIDAR sensor data further comprises:
generating a voxel grid representation of the radar sensor data; and
generating a voxel grid representation of the LIDAR sensor data.

7. The computer-implemented method of claim 6 , wherein the voxel grid representation of the radar sensor data includes a plurality of voxels and each voxel is associated with a voxel occupancy value.

8. The computer-implemented method claim 7 , wherein the voxel occupancy value for a respective voxel is based on a number of radar points that fall within the area associated with the respective voxels.

9. The computer-implemented method of claim 6 , wherein generating, using one or more machine-learned models, a fused representation of the area around the autonomous vehicle based on the radar sensor data and the LIDAR sensor data further comprises:
identifying, using the voxel grid representation of the radar sensor data as input to one or more machine-learned models, feature data for the radar sensor data; and
identifying, using the voxel grid representation of the LIDAR sensor data as input to one or more machine-learned models, feature data for the LIDAR sensor data.

10. The computer-implemented method of claim 1 , wherein generating, using one or more machine-learned models, a fused representation of the area around the autonomous vehicle based on the radar sensor data and the LIDAR sensor data further comprises:
concatenating feature data associated with the radar sensor data and feature data associated with the LIDAR sensor data and associating the concatenated data with a corresponding point in the fused representation.

11. The computer-implemented method of claim 1 , further comprising:
filtering the radar sensor data to remove radar points that have an associated velocity value below a predetermined threshold.

12. A computing system for generating perception and prediction data for autonomous vehicles, the system comprising:
one or more processors and one or more non-transitory computer-readable memories;
wherein the one or more non-transitory computer-readable memories store instructions that, when executed by the processor, cause the computing system to perform operations, the operations comprising:
accessing radar sensor data for an area around an autonomous vehicle, the radar sensor data including a plurality of radar points;
accessing LIDAR sensor data for the area around the autonomous vehicle;
generating, using one or more machine-learned models, a fused representation of the area around the autonomous vehicle based on the radar sensor data and the LIDAR data;
determining, using the one or more machine-learned models, one or more objects in the area of the autonomous vehicle based on the fused representation;
for a respective object in the one or more objects:
determining a plurality of radar points from the radar sensor data that are associated with the respective object, each radar point having an associated velocity;
generating, using a machine-learned model, a similarity score for each determined radar point;
generating a weight associated with each radar point based on the similarity score; and
calculating a predicted velocity for the respective object based on a weighted average of the velocities of the plurality of radar points determined to be associated with the respective object;

generating a proposed motion plan based on the predicted velocity for the respective object; and
transmitting vehicle motion controls to one or more vehicle control systems to implement the motion plan.

13. The computing system of claim 12 , wherein determining a plurality of radar points from the radar sensor data that are associated with the respective object, each radar point having an associated velocity further comprises:
determining for each radar point in the plurality of radar points, a distance from the radar point to a point associated with the respective object; and
selecting one or more radar points based on the determined distance between each radar point and a point associated with the respective object.

14. The computing system of claim 13 , wherein the point associated with the respective object is an estimated center of the object.

15. The computing system of claim 12 , wherein the respective object has an estimated direction.

16. The computing system of claim 15 , wherein generating a similarity score for each determined radar point further comprises:
generating for a radar point, a modified velocity value based on the estimated direction of the respective object and the velocity associated with the radar point.

17. The computing system of claim 16 , wherein the modified velocity value is parallel to the estimated direction of the respective object.

18. The computing system of claim 12 , wherein generating a weight associated with each radar point based on the similarity score further comprises:
normalizing the weights associated with the plurality of radar points.

19. An autonomous vehicle, comprising:
one or more processors; and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the one or more processors to perf

Assignees

• Uatc Llc

Inventors

• Urtasun Raquel
• Yang Bin
• Liang Ming
• Casas Sergio
• Guo Runsheng Benson

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G01S17/931

  of land vehicles · CPC title

• G01S7/417

  involving the use of neural networks · CPC title