WHEEL DETECTION AND ITS APPLICATION IN OBJECT TRACKING AND SENSOR REGISTRATION

• US 20160291145A1
• Filed: 04/06/2015
• Published: 10/06/2016
• Est. Priority Date: 04/06/2015
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: ZENG, SHUQING, SONG, XIAOFENG FRANK
• Granted Patent: US 9,784,829 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/878 Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector G01S13/874 takes precedence
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/9318 Controlling the steering
• CPC Class Codes: G01S 2013/93185 Controlling the brakes
• CPC Class Codes: G01S 7/40 Means for monitoring or calibrating
• CPC Class Codes: G01S 7/4004 of parts of a radar system
• CPC Class Codes: G01S 7/411 Identification of targets based on measurements of radar reflectivity G01S7/415 takes precedence
• CPC Class Codes: G01S 7/415 Identification of targets based on measurements of movement associated with the target
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A method and system are disclosed for tracking a remote vehicle which is driving in a lateral position relative to a host vehicle. Target data from two radar sensors are provided to an object detection fusion system. Wheels on the remote vehicle are identified as clusters of radar points with essentially the same location but substantially varying Doppler range rate values. If both wheels on the near side of the remote vehicle can be identified, a fusion calculation is performed using the wheel locations measured by both radar sensors, yielding an accurate estimate of the position, orientation and velocity of the remote vehicle. The position, orientation and velocity of the remote vehicle are used to trigger warnings or evasive maneuvers in a Lateral Collision Prevention (LCP) system. Radar sensor alignment can also be calibrated with an additional fusion calculation based on the same wheel measurement data.

Claims

1. A method for tracking a remote vehicle which is moving in a region of interest in proximity to a host vehicle, said method comprising:

providing data from two radar sensors onboard the host vehicle, where the radar sensors have coverage areas which overlap such that at least part of the remote vehicle is sensed by both of the radar sensors when the remote vehicle is in the region of interest, and where the data from the radar sensors includes radar points identifying position and Doppler range rate of points on the remote vehicle;

identifying two or more wheels on the remote vehicle from the radar points;

providing wheel location measurements for the two or more wheels from the two radar sensors, where the wheel location measurements identify a range and an azimuth angle of a center of each of the wheels relative to each of the radar sensors;

computing, using a microprocessor, a position, orientation and velocity of the remote vehicle relative to the host vehicle, using a fusion calculation including the wheel location measurements; and

computing an alignment calibration angle for one of the radar sensors using a fusion calculation including the wheel location measurements.

2. The method of claim 1 wherein identifying two or more wheels on the remote vehicle includes clustering the radar points into groups based on positional proximity, and identifying as a wheel any of the groups which include widely varying Doppler range rate measurements.

3. The method of claim 2 wherein clustering the radar points into groups based on positional proximity includes computing a normalized function using a difference in range values and a difference in azimuth angle values for two points, and only including the two points in a group if the normalized function has a value that does not exceed one.

4. The method of claim 2 wherein identifying as a wheel any of the groups which include widely varying Doppler range rate measurements includes computing expected relative radial velocities of a wheel top point and a wheel bottom point on the remote vehicle with respect to the host vehicle, and identifying as a wheel any of the groups which include Doppler range rate measurements substantially matching the expected relative radial velocities of the wheel top point and the wheel bottom point.

5. The method of claim 1 wherein computing a position, orientation and velocity of the remote vehicle relative to the host vehicle includes computing the orientation of the remote vehicle based on the wheel location measurements for two wheels on a side of the remote vehicle nearest the host vehicle.

6. The method of claim 1 wherein computing a position, orientation and velocity of the remote vehicle relative to the host vehicle includes using an algorithm which recursively predicts a state of the remote vehicle which corresponds with the wheel measurement locations, where the state of the remote vehicle includes a two-dimensional position and a two-dimensional velocity.

7. The method of claim 6 wherein the algorithm is a Kalman Filter algorithm.

8. The method of claim 1 further comprising using the position, orientation and velocity of the remote vehicle in a lateral collision prevention (LCP) system, where the region of interest is located to one side of the host vehicle, and the LCP system issues a warning if the remote vehicle enters a warning zone and applies host vehicle brakes and/or steering if the remote vehicle enters a collision-imminent envelope.

9. The method of claim 1 further comprising providing images from a camera onboard the host vehicle, where the images cover a field of view which overlaps the coverage areas of the radar sensors such that the remote vehicle is sensed by the camera and at least one of the radar sensors, and performing a fusion calculation using the images and the radar points to determine the position, orientation and velocity of the remote vehicle.

10. A method for tracking a remote vehicle which is moving in a region of interest in proximity to a host vehicle, said method comprising:

providing data from two radar sensors onboard the host vehicle, where the radar sensors have coverage areas which overlap such that at least part of the remote vehicle is sensed by both of the radar sensors when the remote vehicle is in the region of interest, and where the data from the radar sensors includes radar points identifying position and Doppler range rate of points on the remote vehicle;

identifying two or more wheels on the remote vehicle from the radar points, including clustering the radar points into groups based on positional proximity, and identifying as a wheel any of the groups which include widely varying Doppler range rate measurements;

providing wheel location measurements for the two or more wheels from the two radar sensors, where the wheel location measurements identify a range and an azimuth angle of a center of each of the wheels relative to each of the radar sensors;

computing, using a microprocessor, a position, orientation and velocity of the remote vehicle relative to the host vehicle, using a fusion calculation including the wheel location measurements;

computing an alignment calibration angle for one of the radar sensors using a fusion calculation including the wheel location measurements; and

using the position, orientation and velocity of the remote vehicle in a lateral collision prevention (LCP) system, where the region of interest is located to one side of the host vehicle, and the LCP system issues a warning if the remote vehicle enters a warning zone and applies host vehicle brakes and/or steering if the remote vehicle enters a collision-imminent envelope.

11. The method of claim 10 wherein clustering the radar points into groups based on positional proximity includes computing a normalized function using a difference in range values and a difference in azimuth angle values for two points, and only including the two points in a group if the normalized function has a value that does not exceed one.

12. The method of claim 10 wherein identifying as a wheel any of the groups which include widely varying Doppler range rate measurements includes computing expected relative radial velocities of a wheel top point and a wheel bottom point on the remote vehicle with respect to the host vehicle, and identifying as a wheel any of the groups which include Doppler range rate measurements substantially matching the expected relative radial velocities of the wheel top point and the wheel bottom point.

13. The method of claim 10 wherein computing a position, orientation and velocity of the remote vehicle relative to the host vehicle includes computing the orientation of the remote vehicle based on the wheel location measurements for two wheels on a side of the remote vehicle nearest the host vehicle, and further includes using an algorithm which recursively predicts a state of the remote vehicle which corresponds with the wheel measurement locations, where the state of the remote vehicle includes a two-dimensional position and a two-dimensional velocity.

14. An object detection system for a host vehicle, said system comprising:

two radar sensors onboard the host vehicle, where the radar sensors have coverage areas which overlap such that at least part of the remote vehicle is sensed by both of the radar sensors when the remote vehicle is in the region of interest, and where the data from the radar sensors includes radar points identifying position and Doppler range rate of points on the remote vehicle; and

an object detection processor including a microprocessor and a memory module, said processor being programmed to;

identify two or more wheels on the remote vehicle from the radar points, including clustering the radar points into groups based on positional proximity, and identifying as a wheel any of the groups which include widely varying Doppler range rate measurements;

provide wheel location measurements for the two or more wheels from the two radar sensors, where the wheel location measurements identify a range and an azimuth angle of a center of each of the wheels relative to each of the radar sensors;

compute a position, orientation and velocity of the remote vehicle relative to the host vehicle, using a fusion calculation including the wheel location measurements; and

compute an alignment calibration angle for one of the radar sensors using a fusion calculation including the wheel location measurements.

15. The object detection system of claim 14 wherein clustering the radar points into groups based on positional proximity includes computing a normalized function using a difference in range values and a difference in azimuth angle values for two points, and only including the two points in a group if the normalized function has a value that does not exceed one.

16. The object detection system of claim 14 wherein identifying as a wheel any of the groups which include widely varying Doppler range rate measurements includes computing expected relative radial velocities of a wheel top point and a wheel bottom point on the remote vehicle with respect to the host vehicle, and identifying as a wheel any of the groups which include Doppler range rate measurements substantially matching the expected relative radial velocities of the wheel top point and the wheel bottom point.

17. The object detection system of claim 14 wherein the processor computes the orientation of the remote vehicle based on the wheel location measurements for two wheels on a side of the remote vehicle nearest the host vehicle.

18. The object detection system of claim 14 wherein the processor computes the position, orientation and velocity of the remote vehicle relative to the host vehicle using an algorithm which recursively predicts a state of the remote vehicle which corresponds with the wheel measurement locations, where the state of the remote vehicle includes a two-dimensional position and a two-dimensional velocity.

19. The object detection system of claim 14 wherein the region of interest is located to one side of the host vehicle, the object detection system provides position, orientation and velocity data for the remote vehicle to a lateral collision prevention (LCP) system, and the LCP system issues a warning if the remote vehicle enters a warning zone and applies host vehicle brakes and/or steering if the remote vehicle enters a collision-imminent envelope.

20. The object detection system of claim 14 further comprising a camera onboard the host vehicle, where the camera provides images which cover a field of view which overlaps the coverage areas of the radar sensors such that the remote vehicle is sensed by the camera and at least one of the radar sensors, and the object detection processor performs a fusion calculation using the images and the radar points to determine the position, orientation and velocity of the remote vehicle.