Abstract:
A system and method of automatic image view alignment for a camera-based road condition detection on a vehicle. The method includes transforming a fisheye image into a non-distorted subject image, comparing the subject image with a reference image, aligning the subject image with the reference image, and analyzing the aligned subject image to detect and identify road conditions in real-time as the vehicle is in operation. The subject image is aligned with the reference image by determining a distance (d) between predetermined feature points of the subject and reference images, estimating a pitch of a projection center based on the distance d, and generating an aligned subject image by applying a rectification transformation on the fisheye image by relocating a center of projection of the fisheye image by the pitch angle .
Abstract:
Methods and systems are provided for controlling a vehicle action based on a condition of a road on which a vehicle is travelling, including: obtaining first sensor data as to a surface of the road from one or more first sensors onboard the vehicle; obtaining second sensor data from one or more second sensors onboard the vehicle as to a measured parameter pertaining to operation of the vehicle or conditions pertaining thereto; generating a plurality of road surface channel images from the first sensor data, wherein each road surface channel image captures one of a plurality of facets of properties of the first sensor data; classifying, via a processor using a neural network model, the condition of the road on which the vehicle is travelling, based on the measured parameter and the plurality of road surface channel images; and controlling a vehicle action based on the classification of the condition of the road.
Abstract:
Systems, methods and devices to inhibit sensing reduction in imperfect sensing conditions are described. A multifunctional coating superposing a lens includes a self-cleaning layer and a heating layer. The self-cleaning layer defines an external surface configured to be exposed to an exterior environment. The external surface defines three-dimensional surface features thereon. The three-dimensional surface features are adjacently disposed arcuate features that inhibit adhering of solid particles to the external surface and wetting of the external surface. The heating layer is in thermal communication with the external surface. The heating layer is selectively actuated to provide thermal energy to the external surface through resistive heating. Each of the self-cleaning layer and the heating layer is transparent to a predetermined wavelength of light.
Abstract:
A smart sensor-cover apparatus for covering a sensor, such as a vehicle sensor, includes controllable layers, responsive to inputs, such as a wavelength-filtering controllable layer to selectively filter out select wavelengths of light; a polarizing layer controllable layer to selectively polarize or allow through light; a concealing controllable layer to change between a visible state and a concealed state; and an outermost, cleaning, layer configured to melt incident ice. The outermost layer in various embodiments has an outer surface positioned generally flush with an outer vehicle surface for operation of the apparatus, to promote the concealing effect when the concealing layer is not activate. The outermost layer may be configured to self-mend when scratched, and in some cases is hydrophobic, hydrophilic, or super hydrophilic outer surface. An insulating component, such as a glass or polycarbonate layer, is positioned between each adjacent controllable layer.
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.
Abstract:
A method for determining a wet road surface condition for a vehicle driving on a road. A first image exterior of the vehicle is captured by an image capture device. A second image exterior of the vehicle is captured by the image capture device. A section of the road is identified in the first and second captured images. A texture of the road in the first and second images captured by a processor are compared. A determination is made whether the texture of the road in the first image is different from the texture of the road in the second image. A wet driving surface indicating signal is generated in response to the determination that the texture of the road in the first image is different than the texture of the road in the second image.
Abstract:
A universal machine learning based system for estimating a vehicle state of a vehicle includes one or more controllers executing instructions to receive a plurality of dynamic variables and corresponding historical data. The controllers execute a sensitivity analysis algorithm to determine a sensitivity level for each dynamic variable and corresponding historical data and select two or more pertinent dynamic variables based on the sensitivity level of each dynamic variable and the corresponding historical data. The controllers standardize the two or more pertinent dynamic variables into a plurality of generic dynamic variables, wherein the plurality of generic dynamic variables are in a standardized format that is applicable to any configuration of vehicle, and estimate the vehicle state based on the plurality of generic dynamic variables by one or more machine learning algorithms.
Abstract:
A method of estimating a performance characteristic of a wheel of a vehicle, includes selecting relevant input features based on wheel dynamics and tire behavior, and collecting experimental data for each of the relevant input features at each of a plurality of vehicle operating conditions. The method further includes manually identifying and labeling wheel stability status over time from the experimental data and calculating tractive limit over time from the experimental data. The method also includes training a tractive limit model and training a wheel stability status model. The method further includes receiving a plurality of testing inputs, wherein each of the plurality of testing inputs is received from a sensor on-board the vehicle or from a controller on-board the vehicle and, processing the received testing inputs in a predetermined machine learning process to calculate in one or more data processors a prediction of the performance characteristic.
Abstract:
Systems and methods are provided for generating adapted tuning parameters for target slip estimation, the parameters being adapted to real-time road surface conditions. The method includes, receiving, from a road surface detection module, a road surface condition, Sn, from among N road surface conditions S, range of friction, mu, and a confidence level, Ci. The method receives sensor system data from a sensor system, and determines, as a function of Sn, range of mu, and Ci, initial estimator values including an estimated initial frictional force {circumflex over (Θ)}(0), an initial gain, P0, and an initial projected range of signal bounds, (Pu) and (Pl). The method tunes (i.e., adapts) the initial estimator values to generate therefrom adapted tuning parameters based on received inputs. The method outputs adapted tuning parameters.
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.