Abstract: An example operation includes one or more of receiving driving data of a first vehicle, receiving driving data of a second vehicle driving in a different lane than the first vehicle, determining that the first vehicle and the second vehicle will collide via execution of a machine learning model on the driving data of the first and second vehicles, and displaying a warning on a user interface associated with one or more of the first and second vehicles.

Abstract: Minimizing signaling traffic between a User Plane (UP) and a Control Plane (CP), where Policy and Charging Function/Policy and Charging Rule Function (PCF/PCRF) software transmits a first and a second Policy and Charging Control rule (PCCRule1 and PCCRule2) to CP equipment. The CP equipment installs both PCCRule1 and PCCRule2 on UP equipment as UPRule1 and UPRule2 respectively, where UPRule1 is set as active during a first time period and the UPRule2 set as active during a second time period. User Plane Function (UPF) software matching data traffic with a Packet Detection Rule (PDR) to generate identified traffic during the first and second time periods and automatically redirecting data traffic to a predetermined location at the expiration of the first and second time periods respectively. UPRule1 and UPRule2 are automatically enabled and disabled alternatively after expiration of their respective timers by the UPF software.

Abstract: An example operation includes one or more of receiving sensor data from one or more hardware sensors of a vehicle, obtaining multi-modal data of an occupant of the vehicle from the sensor data, predicting, via a machine learning model, the occupant of the vehicle suffers from attention deficit hyperactivity disorder (ADHD) based on the multi-modal data, and causing one or more hardware systems within the vehicle to provide an audible alert to the occupant based on the prediction.

Abstract: A system and method for controlling delivery of broadcast content are described. They include a broadcast device configured to receive a first media stream from a first content source, provide the first media stream for broadcast transmission in a broadcast communication network while operating in a first operating mode. They further include a diagnostic device configured to monitor at least one characteristic of the quality of the first media stream from at least one location in the broadcast communication network, generate at least one control signal in response to a determination that a value for the at least one characteristic exceeds a threshold value, provide the at least one control signal over the broadcast communication network. The broadcast device is further configured to select a second operating mode in response to receiving at least one control signal, the second operating mode being different from the first operating mode.

Abstract: An example operation includes one or more of obtaining image data of an occupant of a vehicle from one or more hardware sensors associated with the vehicle, extracting occupant data of the occupant from the obtained image data, predicting that the occupant is impaired from cannabis based on execution of a machine learning model on the extracted occupant data, and transmitting an alert to the vehicle based on the prediction.

Abstract: An example operation includes one or more of receiving position data of an ego vehicle and of one or more surrounding vehicles of the ego vehicle via sensors of the ego vehicle while the ego vehicle and the surrounding vehicles are travelling along a road, predicting, via a machine learning model, a trajectory of the ego vehicle and trajectories of the one or more surrounding vehicles based on the position data, generating a routing instruction for the ego vehicle based on the predicted trajectory of the ego vehicle and the predicted trajectories of the one or more surrounding vehicles, and transmitting the routing instruction to a display associated with the ego vehicle.

Abstract: An example operation includes one or more of obtaining sensor data captured by one or more sensors of a vehicle when the vehicle is traveling along a road behind a lead vehicle, determining a size of the lead vehicle, predicting, via execution of a machine learning model, a recommended gap distance of the vehicle between the vehicle and the lead vehicle based on the obtained sensor data and the determined size, and notifying the vehicle of the recommended gap distance.

Abstract: An example operation includes one or more of obtaining sensor data captured by one or more sensors of a vehicle when the vehicle is traveling along a road behind a lead vehicle, determining a size of the lead vehicle, predicting, via execution of a machine learning model, a recommended gap distance of the vehicle between the vehicle and the lead vehicle based on the obtained sensor data and the determined size, and notifying the vehicle of the recommended gap distance.

Abstract: An example operation includes one or more of obtaining sensor data captured by one or more sensors of a transport while the transport is traveling behind a lead transport, detecting that a gap distance between the transport and the lead transport is outside a predetermined threshold based on the obtained sensor data, in response to the detection, determining a recommended gap distance between the transport and the lead transport, and controlling, via the server, a speed of the transport via an activated cruise control function based on the recommended gap distance.

Abstract: Aspects of the present disclosure provide techniques and apparatus for transferring data, such as between power domains via a first in, first out (FIFO) queue. An example method of transferring data includes selecting, via a source multiplexer, a first memory location included in a FIFO queue and storing first data, where the source multiplexer and the FIFO queue are in a first power domain; outputting the first data to a first level shifter; calculating, in the first power domain, a first value based on the first data; outputting the first value to a second level shifter; selecting, via at least one destination multiplexer included in a second power domain, the first level shifter and the second level shifter; calculating, in the second power domain, a second value based on the first data; and comparing the first value to the second value to generate a result.

Abstract: A method comprises determining a vectorized representation of positions of road agents and road geometry based on sensor data from a vehicle, inputting the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network, predicting one or more road agent trajectories at one or more future time steps based on an output of the transformer network, predicting an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps, and causing the vehicle to perform the predicted acceleration at the one or more future time steps.

Abstract: Described are examples for generating an allocation of resources, or designing strategies for resource allocation using artificial intelligence (AI). The allocation of resources or strategies can be generated based on an AI model trained using various aggregations of event data, where the event data identifies one or more properties related to an agent, an action performed by the agent, and an event attributable to the action.

Abstract: Systems, methods, and computer program products that are configured to identify or otherwise detect the presence of bacteria, classify the identified or detected bacteria, and also predict the growth of the classified bacteria on various touchable surfaces within a vehicle passenger cabin or compartment. Such systems, methods, and computer program products are configured to identify/detect, classify, and predict the presence and/or growth of bacteria, and transmit one or more alerts, warnings, and/or reports to vehicle owners, service providers, and/or occupants based on the identification/detection, classification, and prediction.

Abstract: A cloud server may estimate an amount of computing resources needed to perform a computing task, generate a first proposal indicating first parameters for the server to perform the task based on the estimate, obtain, from one or more edge servers, second proposals indicating second parameters for the edge servers to perform the task, transmit the first proposal and the second proposals to a vehicle, upon receiving acceptance of the first proposal, perform the task based on the first parameters, and transmit data associated with performance of the task to the vehicle, and upon receiving acceptance of one of the second proposals, transmit the acceptance to the edge server associated with the accepted proposal.

Abstract: Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Abstract: An example operation includes one or more of receiving an opt-in request to display information on a display of a vehicle configured to be viewed from outside of the vehicle, authenticating the request to allow displaying information on the display, determining information associated with the vehicle to be displayed on the display, determining misuse of the information displayed on the display based on a notification from another vehicle, and flagging the misuse.

Abstract: A system for bulk exporting resource data of parameters included in a view is provided. The system includes at least one processor configured to execute instructions to: receive a user request to bulk export resource data of the parameters for a plurality of instances of the view, based on the received user request, obtain view details for the view, wherein the view details comprise identifiers of the parameters included in the view and identifiers of one or more tables in which the resource data of the parameters are respectively stored, obtain, from the one or more tables and based on the view details, the resource data of the parameters for each of the plurality of instances of the view, and generate an export file including headers with the identifiers of the parameters, based on the obtained view details, and the obtained resource data of the parameters.

Abstract: An example operation includes one or more of receiving, by a vehicle, a software update from a first node, responsive to the software update encountering an issue, ceasing, by the vehicle, the software update resulting in an initial portion of the software update being received by the vehicle, requesting, by the vehicle, a remaining portion of the software update, and receiving, by the vehicle, the remaining portion of the software update from a second node.

Abstract: A computing device and a method to generate a decentralized delivery scheme for multivehicle in an interested area may comprise the following steps. First, the computing device may receive map data, travel request data, and service vehicle data. Second, the computing device may determine one or more constraints based on the travel request data. Third, the computing device may abstract the travel request data, the service vehicle data, and the map data into a request-vehicle graph comprising nodes and edges. Fourth, the computing device may trim the request-vehicle graph into partial request-vehicle graphs for each service vehicle. Fifth, the computing device may encode the partial request-vehicle graphs through a graph neural network (GNN). Sixth, the computing device may train the GNN to predict actions for each service vehicle. Finally, the computing device may instruct the service vehicles to operate based on the actions.

Abstract: An apparatus and method for dynamically monitoring at least one media stream in a signal communication system are described. They include receiving at least one media stream, wherein the media stream(s) are capable of being monitored at a plurality of locations and selecting at least one characteristic associated with the media stream(s) in response to a first user input. They also include generating a first display component in a graphical user interface (GUI), including a visual representation of the at least one characteristic for a first received media stream and generating a second display component in the GUI in response to a second user input, including a visual representation of the at least one characteristic for a second received media stream that is related to the first received media stream, wherein the second display component is dynamically updated in response to a change in the first display component.