Method for evaluating image data of a vehicle camera taking into account information about rain

• US 9,508,015 B2
• Filed: 11/16/2012
• Issued: 11/29/2016
• Est. Priority Date: 12/05/2011
• Status: Expired due to Fees

• Current Assignee: Continental Teves AG & Co. OHG (Continental AG), Conti Temic Microelectronic GmbH (Continental AG)
• Original Assignee: Continental Teves AG & Co. OHG (Continental AG), Conti Temic Microelectronic GmbH (Continental AG)
• Inventors: Strauss, Matthias, Komar, Matthias, Hegemann, Stefan, Kroekel, Dieter
• Primary Examiner(s): Fitzpatrick, Atiba O
• Application Number:
  US14/360,744
• Publication Number:
  US 20150220792A1
• Time in Patent Office: 1,474 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: B60R 2300/8053 for bad weather conditions or night vision
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle

Abstract

In a method for evaluating image data of a vehicle camera, information about raindrops on the vehicle'"'"'s windshield within the field of view of the camera is taken into account in the evaluation of the image data for detection and classification of objects in the environment of the vehicle. Particularly, for example, depending on the number and the size of the raindrops on the windshield, different detection algorithms, image evaluation criteria, classification parameters, or classification algorithms are used for the detection and classification of objects.

Claims

1. A method comprising steps:

a) with a camera in a vehicle, producing first image data representing an object in outside surroundings of the vehicle and second image data representing precipitation particles on an area of a windshield of the vehicle;

b) evaluating the second image data to determine precipitation particle information including a number and/or a size of the precipitation particles on the area of the windshield; and

c) evaluating the first image data by applying at least one evaluation algorithm comprising an edge detection algorithm with at least one evaluation parameter to detect and classify the object, wherein the evaluation parameter comprises an edge detection threshold value for determining which ones of image transitions in the first image data are regarded as edges, and selecting or changing the edge detection threshold value in response to and dependent on the precipitation particle information.

2. The method according to claim 1, further comprising providing plural different parameterizations, and wherein the selecting or changing in the step c) comprises selecting, as the edge detection threshold value, a respective one of the parameterizations in response to and dependent on the precipitation particle information.

3. The method according to claim 1, further comprising providing plural different classifiers for classifying the object, and wherein the step c) further comprises selecting, as the evaluation algorithm for classifying the object, one of the classifiers in response to and dependent on the precipitation particle information.

4. The method according to claim 1, further comprising providing plural different classification algorithms that have been respectively trained for operation under respective different conditions of the precipitation particle information, and wherein the step c) further comprises selecting, as the evaluation algorithm for classifying the object, a respective one of the classification algorithms that has been trained for operation at the respective condition corresponding to the precipitation particle information determined in the step b).

5. The method according to claim 1, further comprising providing plural different detection algorithms for detecting the object, and wherein the step c) further comprises selecting, as the evaluation algorithm for detecting the object, a respective one of the detection algorithms in response to and dependent on the precipitation particle information.

6. The method according to claim 1, wherein the precipitation particles are raindrops.

7. The method according to claim 1, wherein the precipitation particles are hailstones, snowflakes or ice crystals.

8. The method according to claim 1, further comprising comparing the precipitation particle information to a threshold requirement, and switching off a driver assistance function of a driver assistance system of the vehicle when the precipitation particle information fails to satisfy the threshold requirement.

9. The method according to claim 1, further comprising adjusting a maximum speed limitation of an adaptive cruise control of the vehicle in response to and dependent on the precipitation particle information.

10. A method comprising steps:

a) with a camera in a vehicle, producing first image data representing an object in outside surroundings of the vehicle and second image data representing precipitation particles on an area of a windshield of the vehicle;

b) evaluating the second image data to determine precipitation particle information including a number and/or a size of the precipitation particles on the area of the windshield; and

c) evaluating the first image data at least by gathering color information from the first image data to detect and classify the object, and changing the gathering of the color information in response to and dependent on the precipitation particle information.