Abstract: Systems and method are provided for predicting a vehicle pose. In one embodiment, a system includes a non-transitory computer readable medium. The non-transitory computer readable medium includes: an asynchronous prediction module configured to, by a processor, receive vehicle data from a vehicle, and compute a prediction of vehicle pose from the vehicle data, wherein the computation is performed asynchronously using a vehicle time frame and a common coordinate system; and a synchronous prediction module configured to, by the processor, compute a final prediction of the vehicle pose across the vehicle population based on the prediction of vehicle pose from the asynchronous prediction module, wherein the computation is performed synchronously using a global time frame and the common system.

Abstract: Systems and method are provided for predicting a vehicle pose. In one embodiment, a system includes a non-transitory computer readable medium. The non-transitory computer readable medium includes: an asynchronous prediction module configured to, by a processor, receive vehicle data from a vehicle, and compute a prediction of vehicle pose from the vehicle data, wherein the computation is performed asynchronously using a vehicle time frame and a common coordinate system; and a synchronous prediction module configured to, by the processor, compute a final prediction of the vehicle pose across the vehicle population based on the prediction of vehicle pose from the asynchronous prediction module, wherein the computation is performed synchronously using a global time frame and the common coordinate system.

Abstract: Apparatuses, methods, and systems of an edge server communicating with vehicles by continuously receiving messages which include data of state transitions of Electronic Control Units (ECUs) contained in each vehicle; monitoring in real-time the streamed message data from each vehicle for detection of updates of data about state transitions of ECUs used in the vehicular operations; crowdsourcing in real-time the state transition data of the ECUs during the vehicular operations to group together state transition data having commonality as crowdsource transition data; processing, using a machine learning model, the crowdsource transition data, to classify the crowdsource transition data of an anomaly event; and transmit in advance to an identified vehicle an alert of the anomaly event in order for a driver or ADAS to execute a response.

Abstract: Apparatuses, methods, and systems of an edge server communicating with vehicles by continuously receiving messages which include data of state transitions of Electronic Control Units (ECUs) contained in each vehicle; monitoring in real-time the streamed message data from each vehicle for detection of updates of data about state transitions of ECUs used in the vehicular operations; crowdsourcing in real-time the state transition data of the ECUs during the vehicular operations to group together state transition data having commonality as crowdsource transition data; processing, using a machine learning model, the crowdsource transition data, to classify the crowdsource transition data of an anomaly event; and transmit in advance to an identified vehicle an alert of the anomaly event in order for a driver or ADAS to execute a response.

Abstract: At least one transceiver is configured to wirelessly receive a unique identifier (ID) of a source node that is external to a vehicle. A distributed ledger includes a list of unique IDs associated with trusted source nodes. An ID comparison module is configured to compare the unique ID of the source node with the list. The at least one transceiver is configured to, in response to a determination that the unique ID of the source node is not within the list, wirelessly transmit the unique ID of the source node the trusted source nodes of a connected network for execution of a consensus algorithm. A ledger management module is configured to, in response to a determination to trust the source node via execution of the consensus algorithm, add the unique ID of the source node to the list.