Abstract: A system and method is disclosed that may employ a set of ensemble methods that comprise a plurality of machine learning algorithms or statistical algorithms for hazard detection. The system and method may combine images, stereo and context information using a plurality of deep learning algorithms to accurately detection hazards. The system and method may incorporate a depth channel to the red, green, and blue (RGB) channels of an image to create a four-channel RGBD image. The system and method may also overlay an RGB image with a color-map of the depth channel. The system and method may further concatenate regions of interest (ROI) to the RGB channels of an image. Lastly, the system and method may incorporate an auxiliary semantic segmentation decoder for a multi-task learning event on a drivable space.

Abstract: An electronic device includes a user input surface configured to receive a user touch location and a user force input, one or more sensors positioned relative to the user input surface and configured to detect a force applied to the user input surface, and a controller in communication with the one or more sensors. The controller is configured to detect a first sensor response utilizing a first force applied to the user input surface via the one or more sensors and a first position of the first force applied, wherein the first position is relative to the user input surface, simulate a multi-finger force being applied by utilizing at least the first sensor response to form simulated multi-finger data, and deploy a model associated with the multi-finger force, wherein the model is utilized to output a predicted force and predicted location of an applied force relative to the input surface.

Abstract: A computational method for characterizing a biopolymer property of a biopolymer. The computational method includes receiving a dimensional representation of a molecule concentration over time within a fluid flow of a fluid medium flowing through a fluid channel including the biopolymer; predicting a fluid flow velocity and/or a fluid flow pressure of the fluid medium in response to the dimensional representation of the molecule concentration over time within the fluid medium using a machine learning model; and characterizing the biopolymer property of the biopolymer in response to the fluid flow velocity and/or the fluid flow pressure.

Abstract: A lane localization system and method that may include a first measurement distance sensor located on a right-hand side of a vehicle and a second measurement distance sensor located on a left-hand side of the vehicle. The system and method may also be operable to receive data from at least one of the first measurement distance sensor or the second measurement distance sensor. The system and method may further determine which lane along a road the vehicle is traveling within based on a comparison a frequency of one or more echoes indicative of one or more objects located on the right-hand side and the left-hand side of the vehicle.

Abstract: An electronic device includes a user input surface configured to receive a user touch location and a user force input, one or more sensors positioned relative to the user input surface and configured to detect a force applied to the user input surface, and a controller in communication with the one or more sensors. The controller is configured to detect a first sensor response utilizing a first force applied to the user input surface via the one or more sensors and a first position of the first force applied, wherein the first position is relative to the user input surface, simulate a multi-finger force being applied by utilizing at least the first sensor response to form simulated multi-finger data, and deploy a model associated with the multi-finger force, wherein the model is utilized to output a predicted force and predicted location of an applied force relative to the input surface.

Abstract: A system and method is disclosed that may employ a set of ensemble methods that comprise a plurality of machine learning algorithms or statistical algorithms for hazard detection. The system and method may combine images, stereo and context information using a plurality of deep learning algorithms to accurately detection hazards. The system and method may incorporate a depth channel to the red, green, and blue (RGB) channels of an image to create a four-channel RGBD image. The system and method may also overlay an RGB image with a color-map of the depth channel. The system and method may further concatenate regions of interest (ROI) to the RGB channels of an image. Lastly, the system and method may incorporate an auxiliary semantic segmentation decoder for a multi-task learning event on a drivable space.

Abstract: A lane localization system and method that may include a first measurement distance sensor located on a right-hand side of a vehicle and a second measurement distance sensor located on a left-hand side of the vehicle. The system and method may also be operable to receive data from at least one of the first measurement distance sensor or the second measurement distance sensor. The system and method may further determine which lane along a road the vehicle is traveling within based on a comparison a frequency of one or more echoes indicative of one or more objects located on the right-hand side and the left-hand side of the vehicle.

Abstract: A method includes receiving data corresponding to detected objects in a parking region, the data including information ascertained by an ascertaining vehicle driving through the parking region, determining a parking region distribution, based on detected objects in a street segment located within the parking region, determining a street segment distribution, calculating a difference between the parking region and the street segment distribution, identifying an anomaly located within the street segment, and generating an updated parking area map of the parking region based on the identified anomaly. The method includes receiving the data, for example, each time an ascertaining vehicle drives through the parking region.

Abstract: A method for object detection and parking spot localization includes receiving data corresponding to detected objects located within a street segment, the data being ascertained by an ascertaining vehicle driving through the street section, determining a detected object distribution, shifting the detected object distribution, and generating a parking map of available parking spots on the street segment based on a parking region distribution and the shifted detected object distribution. The method includes receiving the data, determining the detected object distribution, and shifting of the detected object distribution, for example each time at least one of the at least one ascertaining vehicle drives through the street section.

Abstract: A method includes receiving data corresponding to spaces situated in a street section, the data being ascertained by at least one ascertaining vehicle driving through the street section and including information corresponding to a beginning edge of at least one object and a ending edge of at least one second object, determining boundaries of at least one space in which parking is permitted based on the received data, and generating a display representation of the boundaries of the at least one space in which parking is permitted. The method includes receiving the data and determining the boundaries, for example, each time an ascertaining vehicle drives through the street.

Abstract: A method for object detection and parking spot localization includes receiving data corresponding to detected objects located within a street segment, the data being ascertained by an ascertaining vehicle driving through the street section, determining a detected object distribution, shifting the detected object distribution, and generating a parking map of available parking spots on the street segment based on a parking region distribution and the shifted detected object distribution. The method includes receiving the data, determining the detected object distribution, and shifting of the detected object distribution, for example each time at least one of the at least one ascertaining vehicle drives through the street section.

Abstract: A method includes receiving data corresponding to detected objects in a parking region, the data including information ascertained by an ascertaining vehicle driving through the parking region, determining a parking region distribution, based on detected objects in a street segment located within the parking region, determining a street segment distribution, calculating a difference between the parking region and the street segment distribution, identifying an anomaly located within the street segment, and generating an updated parking area map of the parking region based on the identified anomaly. The method includes receiving the data, for example, each time an ascertaining vehicle drives through the parking region.

Abstract: A method for aiding finding of available parking areas of a street section includes receiving data corresponding to parking areas situated in a street section, the data including information ascertained by an ascertaining vehicle driving through the street section and information received from a server, determining an instantaneous occupancy estimate of the street section based on the received data, calculating a forecasted occupancy estimate based on the instantaneous occupancy estimate using a timer series forecasting model, and generating a display representation of the calculated forecasted occupancy estimate. The method includes receiving the data and determining the occupancy estimate, for example, each time an ascertaining vehicle drives through the street.

Abstract: A method includes receiving data corresponding to spaces situated in a street section, the data being ascertained by at least one ascertaining vehicle driving through the street section and including information corresponding to a beginning edge of at least one object and a ending edge of at least one second object, determining boundaries of at least one space in which parking is permitted based on the received data, and generating a display representation of the boundaries of the at least one space in which parking is permitted. The method includes receiving the data and determining the boundaries, for example, each time an ascertaining vehicle drives through the street.

Abstract: A method for ascertaining a parking area of at least one street section includes providing information indicating a usable width of the street section, the usable width representing a drivable width of the street section between spaces for parked vehicles at the two lateral sides of a driving vehicle; the driving vehicle driving the street section and ascertaining lateral distances from objects using an ascertainment device situated in the driving vehicle; comparing the ascertained lateral distances to the usable width; and ascertaining the parking area by way of the comparison.

Abstract: A method for aiding finding of available parking areas of a street section includes receiving data corresponding to parking areas situated in a street section, the data including information ascertained by an ascertaining vehicle driving through the street section and information received from a server, determining an instantaneous occupancy estimate of the street section based on the received data, calculating a forecasted occupancy estimate based on the instantaneous occupancy estimate using a timer series forecasting model, and generating a display representation of the calculated forecasted occupancy estimate. The method includes receiving the data and determining the occupancy estimate, for example, each time an ascertaining vehicle drives through the street.

Abstract: A method for ascertaining a parking area of at least one street section includes providing information indicating a usable width of the street section, the usable width representing a drivable width of the street section between spaces for parked vehicles at the two lateral sides of a driving vehicle; the driving vehicle driving the street section and ascertaining lateral distances from objects using an ascertainment device situated in the driving vehicle; comparing the ascertained lateral distances to the usable width; and ascertaining the parking area by way of the comparison.

Abstract: A method for ascertaining a parking area of at least one street section includes providing information indicating a usable width of the street section, the usable width representing a drivable width of the street section between spaces for parked vehicles at the two lateral sides of a driving vehicle; the driving vehicle driving the street section and ascertaining lateral distances from objects using an ascertainment device situated in the driving vehicle; comparing the ascertained lateral distances to the usable width; and ascertaining the parking area by way of the comparison.