Abstract: The disclosure is generally directed to systems and methods associated with communicating privacy rights pertaining to data captured from a vehicle. An example method executed by a processor of a data capture apparatus in a vehicle can include capturing an image. In an example scenario, the image may include an individual who is located outside the vehicle. Furthermore, in some cases, the image can be a part of a video clip containing multiple images. The method further includes generating an identifier for identifying the image(s). The identifier can be a machine-readable symbol such as, for example, a QR-code symbol or a barcode. A notification that includes the identifier may be generated and conveyed to the individual for use by the individual to exercise his/her rights to privacy with respect to the images in which he/she is present.

Abstract: Removal of PII is provided. Sensor data is captured using sensors of a vehicle. Object detection is performed on the sensor data to create a sematic labeling of objects in the sensor data. A model is utilized to classify regions of the sensor data with a public or private labeling according to the sematic labeling and a PII filter corresponding to a jurisdiction of a current location of the vehicle. The sensor data is utilized in accordance with the public or private labeling.

Abstract: A system is disclosed that includes a computer that includes a processor and a memory, the memory including instructions executable by the processor to acquire real world sensor data from a mobile platform for a first time period. The real world sensor data from the mobile platform can be input to a first neural network to predict sensor data of the mobile platform for a second time period that is prior to the first time period. The predicted sensor data and the real world sensor data can be input to a simulation of the mobile platform; wherein the simulation outputs predicted real world operation of the mobile platform based on the predicted sensor data for the second time period and the real world sensor data for the first time period.

Abstract: The present disclosure describes the engineering of microbial cells for fermentative production of histamine and provides novel engineered microbial cells and cultures, as well as related histamine production methods.

Abstract: Rider identification systems and methods using motion sensing are disclosed herein. An example method includes obtaining a location of a mobile device associated with an individual, obtaining sensor data from the mobile device that includes a first motion profile, the sensor data also including environment information around the location, generating a motion model for the individual using the environment information, generating a second motion profile using the motion model, comparing the second motion profile to the first motion profile, and confirming when the second motion profile matches the first motion profile to confirm that the individual is at the location.

Abstract: A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.

Abstract: Methods for managing non-player characters and power centers in a computer game are based on character hierarchies and individualized correspondences between each character's traits or rank and events that involve other non-player characters or objects. Players may share power centers, character hierarchies, non-player characters, and related quests involving the shared objects with other players playing separate and unrelated game instances over a computer network, with the outcome of the quests reflected in different the games. Various configurations of game machines are used to implement the methods.

Abstract: The disclosure is generally directed to systems and methods for detecting that the vehicle is in a reverse mode, receiving a road surface topology, determining a feature of interest is within a collected image and outside a displayed field of view (FOV) for a rear-facing camera due to the road surface topology, adjusting the displayed FOV for the rear-facing camera to place the feature of interest within the displayed FOV. Receiving a reverse mode indication may be over a controller area network (CAN) bus in the vehicle. Receiving a road surface topology includes receiving an estimate of the road surface topology via at least one of monocular depth estimation, photogrammetric range imaging using structure from motion (SFM), multi-view stereo, imaging radar, lidar and a sensor system on the vehicle.

Abstract: A method for generating additional training data for training a machine learning algorithm is disclosed. The method includes (i) providing training data for training the machine learning algorithm, wherein the training data includes labeled sensor data from at least one sensor, (ii) transforming the training data for training the machine learning algorithm in a graph structure, wherein nodes in the graph structure represent objects represented in the corresponding sensor data, and wherein a starting node of the graph structure represents the position of the at least one sensor with respect to the objects represented in the corresponding sensor data, and (iii) generating additional training data for training the machine learning model by modifying the graph structure.

Abstract: The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

Abstract: An object can be detected in contact with a steering wheel of a vehicle. A first image of the steering wheel at a first angle of rotation and a second image of the steering wheel at a second angle of rotation are then obtained. A type of the object is identified based on at least one of the first image or the second image, as one of a human hand or a foreign object. A vehicle component is controlled based on the type of object.

Abstract: An electrochemical additive manufacturing method includes positioning a build plate into an electrolyte solution. The conductive layer comprises at least one conductive-layer segment forming a pattern corresponding with a component. The method further comprises connecting the at least one conductive-layer segment and one or more deposition anodes to a power source. The one or more deposition anodes correspond with at least a portion of the pattern formed by the at least one conductive-layer segment. The method additionally comprises transmitting electrical energy from the power source through the one or more deposition anodes of the plurality of deposition anodes corresponding with the at least the portion of the pattern formed by the at least one conductive-layer segment, through the electrolyte solution, and to the at least one conductive-layer segment, such that material is deposited onto the at least one conductive-layer segment and forms at least a portion of the component.

Abstract: Handling personally identifiable information (PII) in data streams is provided. Processed sensor data is received, from a plurality of vehicles including sensors capturing raw sensor data, the raw sensor data including captured PII and non-PII. The processed sensor data includes simulated PII created based on the captured PII and one or more layers of the captured PII corresponding to the simulated PII. A request is received from a client device for a portion of the processed sensor data. Access keys corresponding to the request are identified. A result is constructed according to the access keys using the processed sensor data. The constructed result is sent to the client device responsive to the request.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive operation data generated by first components of a vehicle; determine that the vehicle is being operated autonomously by an on-board autonomous-operation module based on the operation data without using output from the on-board autonomous-operation module; and upon determining that the vehicle is being operated autonomously, actuate a second component of the vehicle.

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