Abstract: In one embodiment, a processor of a vehicle detects a difference between a physical characteristic of the vehicle predicted by a first machine learning-based model and a physical characteristic of the vehicle indicated by telemetry data generated by a sub-system of the vehicle. The processor forms a packet payload of an update packet indicative of the detected difference, based in part on a relevancy of the physical characteristic to the first machine learning-based model. The processor applies a synchronization strategy to the update packet, to synchronize the update packet with a second machine learning-based model executed by a receiver. The processor sends the update packet to the receiver via a network, to update the second machine learning-based model.

Abstract: In one embodiment, a processor of a vehicle detects a difference between a physical characteristic of the vehicle predicted by a first machine learning-based model and a physical characteristic of the vehicle indicated by telemetry data generated by a sub-system of the vehicle. The processor forms a packet payload of an update packet indicative of the detected difference, based in part on a relevancy of the physical characteristic to the first machine learning-based model. The processor applies a synchronization strategy to the update packet, to synchronize the update packet with a second machine learning-based model executed by a receiver. The processor sends the update packet to the receiver via a network, to update the second machine learning-based model.

Abstract: In one embodiment, a device of a vehicle receives a packet comprising a source address, a destination address, an internet protocol (IP) encapsulated controller area network (CAN) message, and CAN message identifier information. The device compares the source address, the destination address, and the CAN message identifier information to an access control list (ACL). The device makes a determination that delivery of the CAN message to the destination address would be a policy violation based on the comparison. The device drops the packet based on the determination that delivery of the CAN message to the destination address would be a policy violation.

Abstract: In one embodiment, a device of a vehicle receives a packet comprising a source address, a destination address, an internet protocol (IP) encapsulated controller area network (CAN) message, and CAN message identifier information. The device compares the source address, the destination address, and the CAN message identifier information to an access control list (ACL). The device makes a determination that delivery of the CAN message to the destination address would be a policy violation based on the comparison. The device drops the packet based on the determination that delivery of the CAN message to the destination address would be a policy violation.

Abstract: In one embodiment, a processor of a vehicle maintains a machine learning-based behavioral model for the vehicle that is configured to predict a current state of the vehicle based on a plurality of state variables that are available from a plurality of sub-systems of the vehicle and are indicative of physical characteristics of the vehicle. The processor receives, from a first one of the sub-systems, a particular subset of the state variables associated with the first sub-system. The processor performs an index lookup of the state variables in the particular subset within an index of the state variables on which the behavioral model is based. The processor updates a portion of the machine learning-based behavioral model using the received subset of state variables and based on the index lookup.

Abstract: In one embodiment, a processor of a vehicle detects a difference between a physical characteristic of the vehicle predicted by a first machine learning-based model and a physical characteristic of the vehicle indicated by telemetry data generated by a sub-system of the vehicle. The processor forms a packet payload of an update packet indicative of the detected difference, based in part on a relevancy of the physical characteristic to the first machine learning-based model. The processor applies a synchronization strategy to the update packet, to synchronize the update packet with a second machine learning-based model executed by a receiver. The processor sends the update packet to the receiver via a network, to update the second machine learning-based model.

Abstract: In one embodiment, a processor of a vehicle receives a plurality of variables indicative of physical characteristics of the vehicle. The processor uses a machine learning-based model to predict physical states of the vehicle from the plurality of variables indicative of physical characteristics of the vehicle. The model predicts a current physical state of the vehicle from at least two or more prior physical states of the vehicle, and is based on a physical relationship between the physical characteristics. The processor sends synthetic data indicative of the predicted current physical state of the vehicle for use by a receiver application. The processor provides an update to the receiver based on a comparison between the predicted current physical state of the vehicle and the plurality of received variables.