Systems and methods for braking a vehicle based on a detected object

• US 10,155,506 B2
• Filed: 03/28/2017
• Issued: 12/18/2018
• Est. Priority Date: 05/22/2014
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Dagan, Erez
• Primary Examiner(s): Wong, Yuen H
• Application Number:
  US15/471,518
• Publication Number:
  US 20170203744A1
• Time in Patent Office: 630 Days
• CPC Class Codes: B60T 17/221 Procedure or apparatus for checking or keeping in a correct functioning condition of brake systems hydraulic pressure systems in general F15B19/00, F15B21/04; testing structures or apparatus G01M
• CPC Class Codes: B60T 2201/022 Collision avoidance systems
• CPC Class Codes: B60T 2270/40 Failsafe aspects of brake control systems
• CPC Class Codes: B60T 7/22 initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle , or by means of contactless obstacle detectors mounted on the vehicle
• CPC Class Codes: B60T 8/171 Detecting parameters used in the regulation; Measuring values used in the regulation
• CPC Class Codes: B60T 8/172 Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters B60T8/17551 takes precedence
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30261 Obstacle
• CPC Class Codes: G06T 7/269 using gradient-based methods
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

Systems and methods are provided for detecting an object and causing a vehicle to brake based on the detection. In one implementation, an object detecting and braking system for a vehicle includes at least one image capture device configured to acquire a plurality of images of an area including an object in front of the vehicle. The system includes at least one processing device programmed to perform a first image analysis to determine a first estimated time-to-collision of the vehicle with the object, and to perform a second image analysis to determine a second estimated time-to-collision of the vehicle with the object. The processing device is also programmed to calculate a difference between the first estimated time-to-collision and the second estimated time-to-collision, to determine that the difference does not exceed a predetermined threshold, and to cause the vehicle to brake based on the determination that the difference does not exceed the predetermined threshold.

Claims

1. A method for assessing an overall system failure rate associated with a braking decisioning system for a vehicle, comprising:

determining, using a processing device, a first failure rate based on a change in texture between at least two of a first plurality of images of an area in front of the vehicle and based on a total number of times a textural analysis based sub-system makes a false braking decision among a total number of running hours of the textural analysis based sub-system;

determining, using the processing device, a second failure rate based on optical flow information derived from at least two of a second plurality of images of the area in front of the vehicle and based on a total number of times a structural analysis based sub-system makes a false braking decision among a total number of running hours of the structural analysis based sub-system;

determining, using the processing device, the overall system failure rate by multiplying the first failure rate with the second failure rate; and

causing, using the processing device, the vehicle to brake based on the overall system failure rate.

2. The method of claim 1, wherein determining the first failure rate further comprises performing, with the textural analysis based sub-system, a pattern recognition over pixel intensities in each of the at least two of the first plurality of images.

3. The method of claim 2, wherein performing the pattern recognition comprises estimating a change of a footprint size by comparing image data of the at least two of the first plurality of images.

4. The method of claim 3, wherein the footprint size comprises a dimension length of a feature of an object in the at least two of the first plurality of images.

5. The method of claim 1, wherein determining the second failure rate further comprises processing the at least two of the second plurality of images with the structural analysis based sub-system to obtain an optical flow field.

6. The method of claim 1, wherein the optical flow information provides information regarding a movement of trackable textures between the at least two of the second plurality of images.

7. The method of claim 1, wherein the at least two of the second plurality of images used in determining the second failure rate are the same as the at least two of the first plurality of images used in determining the first failure rate.

8. The method of claim 1, wherein determining the second failure rate is performed after determining the first failure rate.

9. A system for assessing an overall system failure rate associated with a braking decisioning system for a vehicle, comprising:

at least one processing device configured to;

determine a first failure rate based on a change in texture between at least two of a first plurality of images of an area in front of the vehicle and based on a total number of times a textural analysis based sub-system makes a false braking decision among a total number of running hours of the textural analysis based sub-system;

determine a second failure rate based on optical flow information derived from at least two of a second plurality of images of the area in front of the vehicle and based on a total number of times a structural analysis based sub-system makes a false braking decision among a total number of running hours of the structural analysis based sub-system;

determine the overall system failure rate by multiplying the first failure rate with the second failure rate; and

cause the vehicle to brake based on the overall system failure rate.

10. The system of claim 9, wherein determining the first failure rate further comprises performing, with the textural analysis based sub-system, a pattern recognition over pixel intensities in each of the at least two of the first plurality of images.

11. The system of claim 10, wherein performing the pattern recognition comprises estimating a change of a footprint size by comparing image data of the at least two of the first plurality of images.

12. The system of claim 11, wherein the footprint size comprises a dimension length of a feature of an object in the at least two of the first plurality of images.

13. The system of claim 9, wherein determining the second failure rate further comprises processing the at least two of the second plurality of images with the structural analysis based sub-system to obtain an optical flow field.

14. The system of claim 9, wherein the optical flow information provides information regarding a movement of trackable textures between the at least two of the second plurality of images.

15. The system of claim 9, wherein the at least two of the second plurality of images used in determining the second failure rate are the same as the at least two of the first plurality of images used in determining the first failure rate.

16. The system of claim 9, wherein determining the second failure rate is performed after determining the first failure rate.

17. A non-transitory computer-readable medium storing instructions that, when executed by at least one processing device, cause the at least one processing device to perform a method for assessing an overall system failure rate associated with a braking decisioning system for a vehicle, the method comprising:

determining a first failure rate based on a change in texture between at least two of a first plurality of images of an area in front of the vehicle and based on a total number of times a textural analysis based sub-system makes a false braking decision among a total number of running hours of the textural analysis based sub-system;

determining a second failure rate based on optical flow information derived from at least two of a second plurality of images of the area in front of the vehicle and based on a total number of times a structural analysis based sub-system makes a false braking decision among a total number of running hours of the structural analysis based sub-system;

determining the overall system failure rate by multiplying the first failure rate with the second failure rate; and

causing the vehicle to brake based on the overall system failure rate.