Abstract: A vehicle can include a body coupled communication (BCC) sensor. A computer in the vehicle can detect that an occupant of a vehicle is touching a screen of a user device, based on a signal from the BCC sensor. Further, it can be determined whether the occupant is in the position of the vehicle operator. Upon determining that the occupant is in the position of the vehicle operator, a gaze direction of the occupant of the vehicle can be determined while the occupant is touching the screen. A prediction can then be output that occupant attention is directed to the screen based on the gaze direction and the signal from the BCC sensor.

Abstract: Utilizing local camera compute for camera calibration is provided. Perception outputs are generated, by respective camera processors of each of a plurality of cameras, the perception outputs indicating properties of calibration references located in raw images captured by the respective camera. In a calibration mode, a camera controller, in communication with the plurality of cameras over a data bus, receives the perception outputs from the plurality of cameras. In a runtime mode, the camera controller utilizes calibration data generated based on the perception outputs to fuse compressed images received from the plurality of cameras over the data bus into a combined surround view.

Abstract: Provided is a method for detecting the trajectories of one or more targets in the field of view of one or more sensors, the method comprising: receiving one or more sensor frames corresponding to the one or more sensors; defining a space of allowable target states for the one or more sensor frames; specifying a set of potential target trajectories, each comprising one allowable target state for each of the one or more sensor frames; specifying target signal parameters for each of the allowable target states, such that the target signal parameters predict the expected target signal contribution corresponding to the one or more sensor frames; specifying a data fidelity objective to quantify how well the target signal parameters match the one or more sensor frames; specifying a sequence of one or more sparsity objectives to penalize a number of detected targets; determine the trajectories of one or more targets as follows: obtain values for all the target signal parameters in all the sensor frames, the obtained

Abstract: A control system for a motor vehicle, for outputting a controlled variable, with the aid of which a directly controlled variable of a motor vehicle is adjustable via suitable control operations, in order to adapt the directly controlled variable to a reference variable of the control system. The control system includes a controller, which is configured to output a first output variable on the basis of the directly controlled variable of the motor vehicle, and on the basis of the reference variable of the control system. The control system further includes a predictive model, which may be trained to output a second output variable that reflects a deviation of a driving behavior of a driver of the motor vehicle from the first output variable of the controller. The controlled variable of the control system encompasses an addition of the first output variable and the second output variable.

Abstract: A method for training a deep-learning-based machine learning algorithm. The method includes: providing training data for training the deep-learning-based machine learning algorithm, wherein the training data comprise sensor data; training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and subsequently optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.

Abstract: Systems and methods for components, e.g., liquid or gas handling, are described. A tester includes a wand that is handheld that can communicate with a handheld electronic device which in turn can communicate with a central monitor for storing and compiling readings as historical profile data. The wand includes an acoustic probe to physically contact the device to acoustically sense the performance of the device. The acoustic probe includes a probe tip and a stack of acoustic elements, an electrode, a stack mass, and a head to convert the acoustic signal into an electrical signal. The tester can include onboard force sensors associated with the probe or a temperature sensor. The tester or a handheld device includes circuitry to process the information, interact with the user, and transmit information to and from the handheld electronic device and/or the central monitor.

Abstract: A method and apparatus for protecting vehicle data from a vehicle includes identifying the vehicle data to be included in output from a processor that is designated for protection, selecting a digital watermark based on a manner in which the output is subject to at least one of observation or interception, applying the digital watermark to the vehicle data to obtain watermarked output for output from the processor, and communicating a watermark identifier over a communication link of the vehicle to a location external from the vehicle. Detection of the digital watermark in data outside of the vehicle based upon the watermark identifier may be used to determine misuse of the vehicle data.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive lidar data from a lidar device, generate a point cloud from the lidar data, identify an object in the point cloud, and, upon identifying the object, delete a portion of the lidar data that is from a region encompassing the object. The object includes personally identifiable information.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a first time series of positions of a vehicle sensor mounted on a vehicle from at least one calibration sensor spaced from the vehicle, measured while vibrations are applied to the vehicle; receive a second time series of motion of the vehicle sensor based on data from the vehicle sensor measured while the vibrations are applied to the vehicle; fuse the first and second time series; and determine at least one calibration value for an adjustment model based on the fusion of the first and second time series. The adjustment model is a model of motion of the vehicle sensor relative to the vehicle resulting from motion of the vehicle.

Abstract: A computer is programmed to receive a plurality of first values for a respective plurality of variables transmitted over a communications network of a vehicle; determine a similarity measure between the first values and a plurality of second values of the variables; determine whether to transmit a collection of data transmitted over the communications network based on the similarity measure; and, upon so determining, transmit the collection of data to a server remote from the vehicle. The second values are a most similar set of values from an activation surface for a feature of the vehicle. The activation surface is a surface in a space defined by the variables dividing the space into regions in which activation of the feature is more or less likely than not, respectively, to have occurred when the variables have values in that region.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive data recorded in a plurality of vehicles, the data including a plurality of decisions about activating a feature of the vehicles and values of a plurality of variables; receive a plurality of decision scores indicating whether the respective decision was optimal according to the data recorded in the respective vehicle; determine an uncertainty function indicating an uncertainty of a likely value of the decision score based on the values of the variables; determine a set of conditions for the variables based on the uncertainty function; and transmit an instruction to the vehicles to record and upload data when the values of the variables satisfy the set of the conditions.

Abstract: Systems and methods for mitigating certain spoofing of vehicle features are disclosed herein. An example method can include determining input torque values obtained from a steering torque sensor associated with a steering wheel of a vehicle, wherein the input torque values are obtained over a period of time, determining road disturbances using a road disturbance model, applying a driver model that is indicative of human driver hands-on-wheel behaviors, determining when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, and the driver model, and executing a remediating measure when the input torque values are indicative of the spoof.

Abstract: A computer-implemented method for using machine learning to determine control parameters for a control system, in particular of a motor vehicle, in particular for controlling a driving operation of the motor vehicle. The method includes: providing a set of travel trajectories; deriving reward functions from the travel trajectories, using an inverse reinforcement learning method; deriving driver type-specific clusters based on the reward functions; determining control parameters for a particular driver type-specific cluster.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to predict a quantity of misalignment of an optical sensor based on a projected motion of a vehicle, predict an error of the predicted quantity of misalignment, and actuate the vehicle based on the predicted quantity of misalignment and the predicted error. The vehicle includes the optical sensor.

Abstract: Systems and methods for testing steam traps or other similar devices in a hot water or steam system are described. A tester includes a wand that is handheld that can communicate with a handheld electronic device which in turn can communicate with a central monitor for storing and compiling readings as historical profile data. The wand includes a probe to physically contact the device to acoustically sense the performance of the device. The probe includes a probe tip and a stack of acoustic elements, an electrode, a stack mass, and a head to covert the acoustic signal into an electrical signal. The handheld device includes circuitry to process the information, interact with the user, and transmit information to and from the handheld electronic device and/or the central monitor.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive sensor data in a time series from a sensor, identify an object in the sensor data, generate anonymized data for the object at a first time in the time series based on the sensor data of the object at the first time, and apply the same anonymized data to an instance of the object in the sensor data at a second time in the time series. The object includes personally identifiable information.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a first time series of positions of a vehicle sensor mounted on a vehicle from at least one calibration sensor spaced from the vehicle, measured while vibrations are applied to the vehicle; receive a second time series of motion of the vehicle sensor based on data from the vehicle sensor measured while the vibrations are applied to the vehicle; fuse the first and second time series; and determine at least one calibration value for an adjustment model based on the fusion of the first and second time series. The adjustment model is a model of motion of the vehicle sensor relative to the vehicle resulting from motion of the vehicle.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first speech data, remove a first vector of speaker-identifying characteristics from the first speech data to generate extracted first speech data, generate a random vector of the speaker-identifying characteristics, and generate second speech data by applying the random vector to the extracted first speech data.

Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor can activate a plurality of light sources in an alternating pattern at an illumination frequency and acquire image data depicting light reflected from an object at a timing corresponding to the alternating pattern at the illumination frequency. In response to variations in the light impinging upon the object from the alternating pattern, an object contour can be determined based on estimating one or more object surface normals based on photometric stereo. One or more of object identity and object depth can be determined based on combining the object contour with the image data.

Abstract: A vehicle control system includes a camera configured to capture image data depicting a field of view proximate the vehicle is disclosed. The vehicle control system further includes a plurality of light sources in connection with the vehicle and a controller. The controller is configured to activate a plurality of lights in an alternating pattern and capture light reflected from at least one object with the camera at a time and corresponding to the alternating pattern of the plurality of lights. In response to variations in the light impinging upon the at least one object from the alternating pattern, the controller is configured to identify a distance of the object.