Multiobject fusion module for collision preparation system

• US 8,812,226 B2
• Filed: 01/19/2010
• Issued: 08/19/2014
• Est. Priority Date: 01/26/2009
• 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
• Primary Examiner(s): Black, Thomas
• Assistant Examiner(s): LI, CE LI
• Application Number:
  US12/689,279
• Publication Number:
  US 20100191391A1
• Time in Patent Office: 1,673 Days
• Field of Search: 701300-302, 340/435, 340/436, 340/903, 382/103, 382/104, 382/106, 382/107
• US Class Current: 701/301
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/86 Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• 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 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/9316 combined with communication equipment with other vehicles or with base stations
• CPC Class Codes: G01S 2013/932 using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/93271 in the front of the vehicles
• 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 for controlling a vehicle operating during a dynamic vehicle event includes monitoring a first input image, monitoring a first tracked object within the first input image in a first tracking cycle, monitoring a second input image, monitoring a second tracked object within the second input image in a second tracking cycle, and determining a dissimilarity measure comparing the first tracked object to the second tracked object. The dissimilarity measure estimates whether the first tracked object and the second tracked object represent a single tracked object proximate to the vehicle. The method further includes associating the first tracked object and the second tracked object based upon the dissimilarity measure, and utilizing the associated objects in a collision preparation system to control operation of the vehicle.

Claims

1. Method for controlling a vehicle operating during a dynamic vehicle event, the method comprising:

monitoring a first input image;

determining within the first input image a first tracked object;

monitoring the first tracked object within the first input image in a first tracking cycle;

determining a position within the first input image of the first tracked object;

defining features within the first input image proximate to the first tracked object;

determining a first label describing a likely classification of the first tracked object;

monitoring a second input image;

determining within the second input image a second tracked object;

monitoring the second tracked object within the second input image in a second tracking cycle;

determining a position within the second input image of the second tracked object;

defining features within the second input image proximate to the second tracked object;

determining a second label describing a likely classification of the second tracked object;

determining a dissimilarity measure comparing the positions of each of the first and second tracked objects, the features proximate to each of the first and second tracked objects, and the first and second labels, the dissimilarity measure estimating whether the first tracked object and the second tracked object represent a single tracked object proximate to the vehicle;

associating the first tracked object and the second tracked object based upon the dissimilarity measure; and

utilizing the associated objects in a collision preparation system to control operation of the vehicle.

2. The method of claim 1, wherein the likely classifications include a quickly moving object, a slowly moving object, and a stationary object.

3. The method of claim 1, further comprising:

monitoring range sensor data corresponding to each of the input images; and

wherein determining within the input images the tracked objects is based upon the range sensor data corresponding to each of the input images.

4. The method of claim 1, wherein the second label is based upon the first label.

5. The method of claim 1, wherein determining within the input images the tracked objects comprises identifying salient image areas and areas associated with motion in each of the images.

6. The method of claim 5, wherein identifying salient image areas and areas associated with motion in each of the images comprises:

determining an edge map;

determining a corner map;

determining a Lucan-Kanade optical flow map;

determining an area of the input image indicating ground; and

identifying the salient image areas and the areas associated with motion based upon the edge map, the corner map, the Lucan-Kanade optical flow map, and the area of the input image indicating ground.

7. The method of claim 6, wherein identifying salient image areas and areas associated with motion further comprises projecting object information from a previous input image upon the input image;

and

wherein identifying the salient image areas and the areas associated with motion is further based upon the projecting object information from the previous input image.

8. The method of claim 1, wherein the first object is selected a priori to compare to the second object.

9. The method of claim 1, further comprising:

analyzing the associated tracked objects with a bank of Kalman filters; and

wherein utilizing the associated tracked objects in the collision preparation system comprises utilizing the analyzed associated tracked objects.

10. The method of claim 9, wherein analyzing the associated objects with a bank of Kalman filters comprises:

monitoring a speed and a yaw rate of the vehicle;

applying the bank of Kalman filters to the associated objects and the speed and yaw rate of the vehicle; and

utilizing a Markov decision process upon the output from the bank of Kalman filters to generate the analyzed associated objects.