Wet road surface condition detection
AssigneeGM Global Technology Operations LLC
InventorsQingrong Zhao, Jinsong Wang, Qi Zhang, Bakhtiar B. Litkouhi
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Abstract
A method for determining a wet surface condition of a road. Capturing an image of a wheel of a remote vehicle traveling in an adjacent lane by an image capture device of a host vehicle. Identifying in the captured image, by processor of a host vehicle, a region of interest relative to the wheel where the region of interest is representative of where precipitation dispersion occurs. A determination is made whether precipitation is present in the region of interest. A wet road surface signal is generated in response to the identification of precipitation in the adjacent lane.
Claims24
A method for determining a wet surface condition of a road with adjacent first and second lanes, the method comprising:
- capturing an image of a road wheel of a remote vehicle traveling in the first lane by an image capture device of a host vehicle traveling in the second lane;
- identifying, in the captured image by a processor of the host vehicle, a region of interest relative to the road wheel of the remote vehicle, wherein the region of interest has a location, a width, and a height, the width and height determined as a function of a tire radius of a tire of the road wheel, and the location determined as a function of the tire radius and a rim radius of a rim of the road wheel, wherein the tire radius and the rim radius are determined by:
- determining a location of the road wheel in the captured image;
- applying an edge detection technique to the road wheel within the captured image;
- applying an image transform analysis to identify respective positions of one or more shapes associated with the road wheel in the captured image;
- identifying contours of the tire and the rim in the captured image;
- identifying a center of the road wheel;
- and
- determining the tire radius and the rim radius each as a function of a respective distance from the center of the road wheel to the identified contours of the tire or the rim;
- determining whether or not precipitation is present in the region of interest;
- and
- generating a wet road surface signal in response to a determination that precipitation is present in the region of interest.
A method for determining a wet surface condition of a road with adjacent first and second lanes, the method comprising:
- capturing an image of a road wheel of a remote vehicle traveling in the first lane by an image capture device of a host vehicle traveling in the second lane;
- identifying, in the captured image by a processor of the host vehicle, a region of interest relative to the road wheel of the remote vehicle, the region of interest having a location determined as a function of a location of the road wheel, wherein determining the location of the road wheel in the captured image includes applying a wheel zone localization, wherein application of the wheel zone localization comprises:
- determining a real-world wheel center position of the road wheel based on signals generated by a sensing-based device of the host vehicle;
- mapping the real-world center position of the road wheel to a wheel center position of the road wheel in the captured image;
- regenerating the captured image so that an optical axis of the captured image is perpendicular to a plane of a face of the road wheel;
- identifying a diameter of a tire of the road wheel in the regenerated image;
- and
- generating a localized wheel zone, the localized wheel zone being sized as a function of the diameter of the tire;
- determining whether or not precipitation is present in the region of interest;
- and
- generating a wet road surface signal in response to a determination that precipitation is present in the region of interest.
A method for determining a wet surface condition of a road with first and second lanes, the method comprising:
- capturing an image of a road wheel of a remote vehicle traveling in the first lane by an image capture device of a host vehicle traveling in the second lane;
- identifying, in the captured image by a processor of the host vehicle, a region of interest relative to the road wheel of the remote vehicle;
- determining whether or not precipitation is present in the region of interest in the captured image, including:
- analyzing the region of interest as a gray-level image;
- applying a filter to the captured image to identify noise in the gray-level image, the noise representing precipitation in the region of interest;
- and
- determining whether or not precipitation is present in the region of interest based on a non-uniformity of color in the filtered image;
- and
- generating a wet road surface signal in response to a determination that precipitation is present in the region of interest.
Dependencies are inferred from the claim text.