Abstract: This disclosure describes systems and using dual vehicle digital twins for model-based learning and remotely-operated vehicles. An example method may include receiving, by a processor associated with a first digital twin stored locally on a computing system of a vehicle or a second digital twin stored remotely on a first remote system, data relating to a first sub-system of the vehicle and a second sub-system of the vehicle. The example method may also include sending the data to the first digital twin or the second digital twin, wherein sending the data includes synchronizing with the first digital twin or the second digital twin.

Abstract: A vehicle includes a controller, programmed to responsive to identifying a first user via a mobile device, load a first non-person entity associated with the first user from the mobile device to configure vehicle settings, the first non-person entity including a driver setting, a first entertainment setting and a second entertainment setting; and responsive to identifying a second user entering the vehicle, switch from the first entertainment setting to a second entertainment setting.

Abstract: Systems, methods, and computer-readable media are described for performing real-time vehicular incident risk prediction using real-time vehicle-to-everything (V2X) data. A vehicular incident risk prediction machine learning model is trained using historical V2X data such as historical incident data and historical vehicle operator driving pattern behavior data as well as third-party data such as environmental condition data and infrastructure condition data. The trained machine learning model is then used to predict the risk of an incident for a vehicle on a roadway segment based on real-time V2X data relating to the roadway segment and/or vehicle operators on the roadway segment. A notification of a high risk of incident can then be sent to a V2X communication device of the vehicle to inform an operator of the vehicle.

Abstract: A vehicle includes a controller, programmed to responsive to identifying a first user via a mobile device, load a first non-person entity associated with the first user from the mobile device to configure vehicle settings, the first non-person entity including a driver setting, a first entertainment setting and a second entertainment setting; and responsive to identifying a second user entering the vehicle, switch from the first entertainment setting to a second entertainment setting.

Abstract: A computer is programmed to allocate respective connectivity quality data of a geographic area to a first map or a second map. The computer is further programmed to assign one of a plurality of subsets of the first map and one of a plurality of subsets of the second map to a first vehicle, identify respective locations of the first and second vehicles and one of the first or second maps that includes the locations of the first and second vehicles. The computer is further programmed to send, to the first and second vehicles, a map dataset that is a result of applying an XOR function to (1) the subset of the identified map that includes the location of the first vehicle assigned to the first vehicle and (2) the subset of the identified map that includes the location of the second vehicle assigned to the second vehicle.

Abstract: A graph of devices is constructed, each network device serving an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the network devices, each edge of the graph connecting network devices by interference weight, such that edges between connected network devices using different channels have an interference weight of zero, and edges between connected network devices using the same channel have an interference weight denoting an amount of interference between the connected network devices. A cell utilization of each of the network devices is determined according to an amount of traffic served by the respective network device compared to a total of the amounts of bandwidth for all network devices. A vehicle requesting bandwidth is assigned to the one of the network devices having a smallest product of cell utilization and maximum interference weight edge.

Abstract: A graph of devices is constructed, each network device serving an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the network devices, each edge of the graph connecting network devices by interference weight, such that edges between connected network devices using different channels have an interference weight of zero, and edges between connected network devices using the same channel have an interference weight denoting an amount of interference between the connected network devices. A cell utilization of each of the network devices is determined according to an amount of traffic served by the respective network device compared to a total of the amounts of bandwidth for all network devices. A vehicle requesting bandwidth is assigned to the one of the network devices having a smallest product of cell utilization and maximum interference weight edge.

Abstract: A system including one or more servers, programmed to responsive to receiving a token request from a vehicle to access content stored in a content cloud, validate the token request against pre-defined policies; responsive to a successful policy validation, verify token generating responsibility based on a validation result and pre-defined rules; and responsive to verifying the system has the token generating responsibility, generate a token for the token request.

Abstract: A computer is programmed to allocate respective connectivity quality data of a geographic area to a first map or a second map. The computer is further programmed to assign one of a plurality of subsets of the first map and one of a plurality of subsets of the second map to a first vehicle, identify respective locations of the first and second vehicles and one of the first or second maps that includes the locations of the first and second vehicles. The computer is further programmed to send, to the first and second vehicles, a map dataset that is a result of applying an XOR function to (1) the subset of the identified map that includes the location of the first vehicle assigned to the first vehicle and (2) the subset of the identified map that includes the location of the second vehicle assigned to the second vehicle.

Abstract: A graph of devices is constructed, each device requiring an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the devices, each edge of the graph connecting devices according to a weight denoting interference between the connected devices. The vertices of the graph are labeled with labels such that no two vertices sharing the same edge have the same label, each label requiring bandwidth corresponding to the device of that label requiring the most bandwidth. If the sum of the bandwidth required for the labels exceeds the set bandwidth, the edge of the graph having the least interference is deleted, and the perform the construct and label operations are repeated. If the sum of the bandwidth required for the labels is within the set bandwidth, the bandwidth is assigned to the devices.

Abstract: A graph of devices is constructed, each device requiring an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the devices, each edge of the graph connecting devices according to a weight denoting interference between the connected devices. The vertices of the graph are labeled with labels such that no two vertices sharing the same edge have the same label, each label requiring bandwidth corresponding to the device of that label requiring the most bandwidth. If the sum of the bandwidth required for the labels exceeds the set bandwidth, the edge of the graph having the least interference is deleted, and the perform the construct and label operations are repeated. If the sum of the bandwidth required for the labels is within the set bandwidth, the bandwidth is assigned to the devices.

Abstract: Systems, methods, and computer-readable media are described for performing real-time vehicular incident risk prediction using real-time vehicle-to-everything (V2X) data. A vehicular incident risk prediction machine learning model is trained using historical V2X data such as historical incident data and historical vehicle operator driving pattern behavior data as well as third-party data such as environmental condition data and infrastructure condition data. The trained machine learning model is then used to predict the risk of an incident for a vehicle on a roadway segment based on real-time V2X data relating to the roadway segment and/or vehicle operators on the roadway segment. A notification of a high risk of incident can then be sent to a V2X communication device of the vehicle to inform an operator of the vehicle.

Abstract: A vehicle includes a controller, programmed to responsive to identifying a first user via a mobile device, load a first non-person entity associated with the first user from the mobile device to configure vehicle settings, the first non-person entity including a driver setting, a first entertainment setting and a second entertainment setting; and responsive to identifying a second user entering the vehicle, switch from the first entertainment setting to a second entertainment setting.

Abstract: A system includes at least one processor configured to, in response to receiving a VIN from a remote vehicle, transmit to the vehicle a parameter definition selected based on fields of the VIN to configure an ECU of the vehicle to enter a logging mode to capture, aggregate, and send operational data of the vehicle, and a bandwidth configuration file for a modem of the vehicle based on historical throughput requirements associated with operational data. The parameter definition may include a reporting application configured to be executed by a processor of the ECU to generate a processed parameter from a raw parameter associated with vehicle operation. Also, the parameter definition may include updated firmware for the ECU, and the reporting application is configured to be executed by the ECU after the updated firmware is installed in the ECU.

Abstract: A system including one or more servers, programmed to responsive to receiving a token request from a vehicle to access content stored in a content cloud, validate the token request against pre-defined policies; responsive to a successful policy validation, verify token generating responsibility based on a validation result and pre-defined rules; and responsive to verifying the system has the token generating responsibility, generate a token for the token request.

Abstract: A mesh network localization system includes at least one base station located on a building roof to receive a GPS signal from a satellite and to transmit a base station location signal. An infrastructure device is located at a ground level to derive its global position based on the base station location signal. A mobile end-device is in communication with the infrastructure device via DSRC, and the infrastructure device transmits a derived global position to the mobile end-device unable to receive a GPS signal.

Abstract: A system includes a vehicle processor configured to receive a query, including response network parameters, from a remote entity. The processor is also configured to determine a response transport policy based on the parameters. The processor is further configured to determine if a network having characteristics defined by the policy in conjunction with parameter values is currently connected and utilize the network, if currently available, to transmit a query response, otherwise queue the response until such a network is connected.

Abstract: A system includes at least one processor configured to, in response to receiving a VIN from a remote vehicle, transmit to the vehicle a parameter definition selected based on fields of the VIN to configure an ECU of the vehicle to enter a logging mode to capture, aggregate, and send operational data of the vehicle, and a bandwidth configuration file for a modem of the vehicle based on historical throughput requirements associated with operational data. The parameter definition may include a reporting application configured to be executed by a processor of the ECU to generate a processed parameter from a raw parameter associated with vehicle operation. Also, the parameter definition may include updated firmware for the ECU, and the reporting application is configured to be executed by the ECU after the updated firmware is installed in the ECU.