Abstract:
A method for determining a snow covered surface condition of a path of travel. A beam of light is emitted at a surface of the path of travel by a light emitting source. An image of a path of travel surface is captured by an image capture device. The image capture device is mounted on the vehicle and captures an image in a downward direction. The captured image captures the beam of light emitted on the path of travel surface. Analyzing a subsurface scattering of the light generated on the path of travel surface by a processor. A determination is made whether snow is present on the path of travel. A snow covered path of travel surface signal is generated in response to the identification of snow on the path of travel.
Abstract:
Systems and methods use an image of a road surface that is generated by a camera. The image includes a pattern from a light source. A region of interest in the image is determined based on the pattern from the light source. A total area is determined that includes at least part of the region of interest and an area adjacent the region of interest. A feature vector is extracted based on the region of interest and the total area. A road condition is determined based on the feature vector and a classifier.
Abstract:
A method of determining a surface condition of a path of travel. A plurality of images is captured of a surface of the path of travel by an image capture device. The image capture device captures images at varying scales. A feature extraction technique is applied by a feature extraction module to each of the scaled images. A fusion technique is applied, by the processor, to the extracted features for identifying the surface condition of the path of travel. A road surface condition signal provide to a control device. The control device applies the road surface condition signal to mitigate the wet road surface condition.
Abstract:
Methods and systems for determining road surface information in a vehicle. In one embodiment, the method includes: determining at least one condition assessment value based on steering data; determining a feature set to include at least one of self-aligning torque (SAT), slip angle, SAT variance, steering rate, and lateral acceleration based on the condition assessment value; processing steering data obtained during a steering maneuver and associated with the feature set using a pattern classification technique; and determining a surface type based on the processing.
Abstract:
A method for determining wetness on a path of travel. A surface of the path of travel is captured by at least one image capture device. A plurality of wet surface detection techniques is applied to the at least one image. An analysis for each wet surface detection technique is determined in real-time of whether the surface of the path of travel is wet. Each analysis independently determines whether the path of travel is wet. Each analysis by each wet surface detection technique is input to a fusion and decision making module. Each analysis determined by each wet surface detection technique is weighted within the fusion and decision making module by comprehensive analysis of weather information, geology information, and vehicle motions. A wet surface detection signal is provided to a control device. The control device applies the wet surface detection signal to mitigate the wet surface condition.
Abstract:
A method for determining a thickness of water on a path of travel. A plurality of images of a surface of the path of travel is captured by an image capture device over a predetermined sampling period. A plurality of wet surface detection techniques are applied to each of the images. A detection rate is determined in real-time for each wet surface detection technique. A detection rate trigger condition is determined as a function of a velocity of the vehicle for each detection rate. The real-time determined detection rate trigger conditions are compared to predetermined detection rate trigger conditions in a classification module to identify matching results pattern. A water film thickness associated with the matching results pattern is identified in the classification module. A water film thickness signal is provided to a control device. The control device applies the water film thickness signal to mitigate the wet surface condition.
Abstract:
A method for determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and captures an image in a downward direction. A region of interest rearward of the wheel of the host vehicle is identified in the captured image by a processor. The region of interest is representative of where rearward splash as generated by the wheel occurs. A determination is made whether precipitation is present in the region of interest by applying a filter to the image. A wet road surface signal is generated in response to the identification of precipitation in the region of interest.
Abstract:
A method for determining a wet road surface condition on a road. An image exterior of the vehicle is captured by an image capture device. A real object and a virtual object are detected in the captured image. A feature point is identified on the real object and on the virtual object. A potential virtual object associated with the real object is identified on a ground surface of the road in the captured image. The feature point detected on the real object is compared with the feature point detected on the virtual object. A determination is made whether the ground surface includes a mirror effect reflective surface in response to the feature point detected on the real object matching the feature point detected on the virtual object. A wet driving surface indicating signal is generated in response to the determination that the ground surface includes a mirror effect reflective surface.
Abstract:
Methods and systems are provided for determining a road surface condition. In one embodiment, a method includes: receiving vehicle data; constructing, by the processor, a driver behavioral model based on the vehicle data; determining, by the processor, a surface condition based on the driver behavioral model; and generating a signal based on the surface condition.
Abstract:
Methods and systems are provided for determining a road surface friction coefficient and controlling a feature of the vehicle based thereon. In one embodiment, a method includes: receiving signals from an electronic power steering system and an inertial measurement unit; estimating parameters associated with an electronic power steering system model using an iterative optimization method; calculating an electronic power steering system variable using the electronic power steering system model, the estimated parameters and one or more of the received signals; determining whether the calculated electronic power steering system variable satisfies a fitness criterion; and when the calculated electronic power steering system variable does satisfy the fitness criterion, determining a road surface friction coefficient based on at least one of the estimated parameters.