System and method for providing target threat assessment in a collision avoidance system on a vehicle

• US 9,983,306 B2
• Filed: 12/28/2015
• Issued: 05/29/2018
• Est. Priority Date: 05/23/2013
• 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, Zhang, Wende, Shields, Mark E.
• Primary Examiner(s): Gregory, Bernarr E
• Application Number:
  US14/980,327
• Publication Number:
  US 20160109571A1
• Time in Patent Office: 883 Days
• CPC Class Codes: G01S 13/87 Combinations of radar systems, e.g. primary radar and secondary radar
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/93185 Controlling the brakes
• CPC Class Codes: G01S 2013/93271 in the front of the vehicles
• CPC Class Codes: G01S 7/40 Means for monitoring or calibrating
• CPC Class Codes: G01S 7/4039 of sensor or antenna obstruction, e.g. dirt- or ice-coating
• CPC Class Codes: G01S 7/41 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Abstract

A system and method for providing target selection and threat assessment for vehicle collision avoidance purposes that employ probability analysis of radar scan returns. The system determines a travel path of a host vehicle and provides a radar signal transmitted from a sensor on the host vehicle. The system receives multiple scan return points from detected objects, processes the scan return points to generate a distribution signal defining a contour of each detected object, and processes the scan return points to provide a position, a translation velocity and an angular velocity of each detected object. The system selects the objects that may enter the travel path of the host vehicle, and makes a threat assessment of those objects by comparing a number of scan return points that indicate that the object may enter the travel path to the number of the scan points that are received for that object.

Claims

1. A method for providing target threat assessment in a collision avoidance system on a host vehicle, said method comprising:

determining a predicted travel path of the host vehicle using motion dynamics of the host vehicle;

transmitting a scan signal from at least one sensor on the host vehicle;

receiving multiple scan return points at the host vehicle from one or more detected objects that reflect the scan signal;

generating a probability function distribution signal defining a contour of each detected object using the scan signal;

calculating a position and velocity of each detected object using the scan return points;

selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using a probabilistic technique and the distribution signal, position and velocity of each detected object; and

determining a threat assessment of the selected objects by analyzing a time to collision (TTC) for each of the selected objects.

2. The method according to claim 1 wherein generating a distribution signal of each object includes updating the distribution signal at subsequent sample times using a Gaussian mixture model.

3. The method according to claim 1 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle includes reducing the complexity of the distribution signal by using a reduced number of the scan points.

4. The method according to claim 3 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the probabilistic technique includes representing each of the reduced number of scan points as a Gaussian distribution.

5. The method according to claim 1 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the probabilistic technique includes using a Monte Carlo technique that separates the distribution signal into a plurality of particles.

6. The method according to claim 1 wherein determining a threat assessment includes identifying a number of the scan points that are less than a predetermined threshold.

7. The method according to claim 1 further comprising performing sensor visibility analysis to determine if one or more of the sensors is being blocked in a particular direction, wherein determining a threat assessment includes determining a collision threat if one of the sensors is blocked.

8. The method according to claim 7 wherein performing sensor visibility analysis includes performing the sensor visibility analysis if it is determined that the host vehicle is at or approaching an intersection.

9. The method according to claim 1 wherein determining a threat assessment includes providing a warning of a potential collision and providing automatic braking for an imminent collision.

10. The method according to claim 1 wherein the at least one sensor is a radar sensor.

11. A method for providing target threat assessment in a collision avoidance system on a host vehicle, said method comprising:

determining a predicted travel path of the host vehicle using motion dynamics of the host vehicle;

transmitting a radar signal from at least one radar sensor on the host vehicle;

receiving multiple scan return points at the host vehicle from one or more detected objects that reflect the radar signal;

generating a probability function distribution signal defining a contour of each detected object using the radar signal, wherein generating a distribution signal of each detected object includes updating the distribution signal at subsequent sample times using a Gaussian mixture model;

calculating a position, a translation velocity and an angular velocity of each detected object using the scan return points;

selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the distribution signal, position, translation velocity and angular velocity of each object, wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle includes using a probabilistic technique; and

determining a threat assessment of the selected objects by analyzing a time to collision (TTC) for each of the selected objects.

12. The method according to claim 11 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle includes reducing the complexity of the distribution signal to a reduced number of the scan points.

13. The method according to claim 12 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the probabilistic technique includes representing each of the reduced number of scan points as a Gaussian distribution.

14. The method according to claim 11 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the probabilistic technique includes using a Monte Carlo technique that separates the distribution signal into a plurality of particles.

15. The method according to claim 11 further comprising performing sensor visibility analysis to determine if one or more of the sensors is being blocked in a particular direction, wherein determining a threat assessment includes determining a collision threat if one of the sensors is blocked.

16. The method according to claim 15 wherein performing sensor visibility analysis includes performing the sensor visibility analysis if it is determined that the host vehicle is at or approaching an intersection.

17. An analysis system for providing target threat assessment in a collision avoidance system on a host vehicle, said analysis system comprising:

vehicle dynamics sensors on the host vehicle providing vehicle motion dynamics data;

at least one sensor on the host vehicle transmitting a scan signal and receiving multiple scan return points from one or more detected objects that reflect the scan signal; and

a processor configured with an algorithm including steps of;

determining a predicted travel path of the host vehicle using the vehicle motion dynamics data;

generating a probability function distribution signal defining a contour of each detected object using the scan signal;

calculating a position and velocity of each detected object using the scan return points;

selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using a probabilistic technique and the distribution signal, position and velocity of each detected object; and

determining a threat assessment of the selected objects by analyzing a time to collision (TTC) for each of the selected objects.

18. The analysis system according to claim 17 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle reduces the complexity of the distribution signal by using a reduced number of the scan points.

19. The analysis system according to claim 18 wherein selecting the detected objects which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the probabilistic technique represents each of the reduced number of scan points as a Gaussian distribution.

20. The analysis system according to claim 17 wherein selecting the detected objects that may which have a probability exceeding a threshold of being in or entering the predicted travel path of the host vehicle using the probabilistic technique uses a Monte Carlo technique that separates the distribution signal into a plurality of particles.