Abstract: A method includes receiving a service request from a vehicle within a geographic area, receiving a first set of data from the vehicle comprising a position of the vehicle within the geographic area, a direction that the vehicle is facing, and a field of view for an augmented reality display associated with the vehicle, obtaining historical traffic data associated with the geographic area, identifying one or more traffic infrastructures within the field of view of the augmented reality display based on a map associated with the geographic area, the historical traffic data, the position of the vehicle, and the direction the vehicle is facing, generating an augmented reality image for the one or more traffic infrastructures based on the historical traffic data and the field of view of the augmented reality display, and sending the augmented reality image to the vehicle.

Abstract: A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

Abstract: System, methods, and other embodiments described herein relate to improving execution of processing requests by an edge server. In one embodiment, a method includes predicting a number of computing requests from vehicles for execution by the edge server using a prediction solver for a time period that is forthcoming. The prediction solver may predict the number of computing requests using a prediction model selected in association with service constraints of the edge server and information from an additional server. The method also includes determining a request handling scheme using an optimization solver according to the number of computing requests, the service constraints of the edge server, and a service area of the edge server. The method also includes communicating the request handling scheme and a resource schedule to the edge server on a condition that a resources criteria are satisfied for the time period.

Abstract: Systems and methods described herein relate to managing an automotive edge computing environment. One embodiment receives current status information from one or more edge servers; receives and queues requested computing tasks from one or more connected vehicles; selects, as an optimization trigger number N, a largest number of requested computing tasks for which an optimization process can be completed within a time, per requested computing task, that is less than an average time gap between the requested computing tasks; performs the optimization process when a number of queued requested computing tasks exceeds the optimization trigger number N, wherein the optimization process produces an updated data transfer schedule and an updated data process schedule for N queued requested computing tasks; and transmits the updated data transfer schedule and the updated data process schedule to the one or more edge servers and the one or more connected vehicles.

Abstract: Systems and methods described herein relate to managing an automotive edge computing environment. One embodiment receives current status information from one or more edge servers; receives and queues requested computing tasks from one or more connected vehicles; selects, as an optimization trigger number N, a largest number of requested computing tasks for which an optimization process can be completed within a time, per requested computing task, that is less than an average time gap between the requested computing tasks; performs the optimization process when a number of queued requested computing tasks exceeds the optimization trigger number N, wherein the optimization process produces an updated data transfer schedule and an updated data process schedule for N queued requested computing tasks; and transmits the updated data transfer schedule and the updated data process schedule to the one or more edge servers and the one or more connected vehicles.

Abstract: System, methods, and other embodiments described herein relate to improving execution of processing requests by an edge server. In one embodiment, a method includes predicting a number of computing requests from vehicles for execution by the edge server using a prediction solver for a time period that is forthcoming. The prediction solver may predict the number of computing requests using a prediction model selected in association with service constraints of the edge server and information from an additional server. The method also includes determining a request handling scheme using an optimization solver according to the number of computing requests, the service constraints of the edge server, and a service area of the edge server. The method also includes communicating the request handling scheme and a resource schedule to the edge server on a condition that a resources criteria are satisfied for the time period.

Abstract: A method includes receiving a service request from a vehicle within a geographic area, receiving a first set of data from the vehicle comprising a position of the vehicle within the geographic area, a direction that the vehicle is facing, and a field of view for an augmented reality display associated with the vehicle, obtaining historical traffic data associated with the geographic area, identifying one or more traffic infrastructures within the field of view of the augmented reality display based on a map associated with the geographic area, the historical traffic data, the position of the vehicle, and the direction the vehicle is facing, generating an augmented reality image for the one or more traffic infrastructures based on the historical traffic data and the field of view of the augmented reality display, and sending the augmented reality image to the vehicle.

Abstract: A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

Abstract: Systems and methods are disclosed. A controller includes one or more processors; and one or more non-transitory memory modules storing machine-readable instructions that, when executed, cause the one or more processors to obtain environment data associated with a vehicle; obtain trajectory data associated with the vehicle; and identify one or more edge servers associated with the trajectory data. The machine-readable instructions, when executed, cause the one or more processors to obtain edge server state information of the one or more edge servers; select a first set of the environment data to transmit to a first edge server of the one or more edge servers based on the edge server state information of the one or more edge servers; and generate an instruction signal causing the vehicle to transmit the first set of the environment data to the first edge server of the one or more edge servers.