Abstract: A method for deploying of a backup roadside unit (RSU) vehicle to provide on-demand RSU services is described. The method may include determining, by a server, a resource specification to deploy a selected backup RSU vehicle to a requested site. The determining of the resource specification may be performed by the server in response to a scene description received from a requestor. The method also includes sending a dispatch instruction to the selected backup RSU vehicle to deploy the selected backup RSU vehicle to the requested site. In this method, a hardware/software capability of the selected backup RSU vehicle is matched to the resource specification determined by the server.

Abstract: System, methods, and other embodiments described herein relate to selecting servers and allocating resources concurrently for offloading computing tasks from vehicles. In one embodiment, a method includes acquiring characteristics of a vehicle and a server for an offloading request, wherein the offloading request is associated with a computing task of the vehicle. The method also includes, upon satisfying criteria for optimization associated with executing the computing task remotely, determining server selection and resource allocation by processing the characteristics using modeling. The method also includes communicating the server selection and the resource allocation to the vehicle.

Abstract: The disclosure includes embodiments for automatically generating a virtual pedestrian for inclusion in a digital simulation. Some embodiments of a method may include automatically generating a virtual pedestrian based on a path specification and a behavior specification. The method may include executing a digital simulation that includes the virtual pedestrian crossing an intersection and a virtual vehicle responding to the virtual pedestrian crossing the intersection, wherein the digital simulation is operable to measure a performance of a virtual Advanced Driver Assistance System (virtualized “ADAS system”) included in the virtual vehicle to protect the virtual pedestrian when responding to the virtual pedestrian crossing the intersection.

Abstract: An anchor vehicle includes a network device configured to access a network using dedicated wireless connectivity, and a processor. The processor is programmed to: receive a request for accessing the network using the dedicated wireless connectivity from a connected vehicle; determine whether the connected vehicle has consented to monitoring compliance with a warranty of the connected vehicle; and provide, to the connected vehicle, access to the dedicated wireless connectivity via the anchor vehicle in response to determining that the connected vehicle has consented to monitoring the compliance with the warranty of the connected vehicle.

Abstract: The disclosure includes embodiments for proactive vehicle maintenance scheduling based on one or more digital twin simulations. A method includes generating a digital twin of a vehicle. The method includes receiving digital data recorded by a sensor and describing the vehicle as it exists in a real-world and one or more historical journeys of the vehicle in the real-world. The method includes updating the digital twin of the vehicle based on the digital data describing the vehicle so that the digital twin is consistent with a condition of the vehicle as it exists in the real-world. The method includes executing a simulation based on the digital twin and the one or more historical journeys. The method includes estimating a component of the vehicle that fails at a future time based on the simulation. The method includes scheduling a reservation to repair the component before the future time.

Abstract: A method for preventing an operation of a car application that causes a quality of service of an operation of a computer system of a vehicle to be reduced below a threshold can include receiving, from a separate source, a value indicative of the quality of service of the operation of the computer system during the operation of the car application in conjunction with the operation of the computer system. The method can include determining an existence of a condition. The condition can be that the value is less than the threshold. The method can include causing, in response to a determination of a lack of the condition, the car application to be in a condition to be operated on the vehicle. The method can include preventing, in response to a determination of the existence of the condition, the car application from being in the condition to be operated.

Abstract: The disclosure describes embodiments for modifying a whether an ego vehicle changes lanes to a target lane at a target time based on a payload of a Vehicle-to-Everything (V2X) message originated by a remote vehicle. In some embodiments, a method includes determining, based on the payload, whether the remote vehicle is changing lanes to the target lane at the target time. The method includes determining that the ego vehicle is changing lanes to the target lane at approximately the target time. The method includes estimating that the ego vehicle and the remote vehicle will collide at the target lane at the target time. The method includes modifying an operation of a vehicle component of the ego vehicle so that the ego vehicle does not change lanes to the target lane at the target time.

Abstract: System, methods, and other embodiments described herein relate to improving scheduling tasks within an edge-computing environment. In one embodiment, a method includes, upon establishing a communication connection with a vehicle by an edge device of the edge-computing environment, collecting offloading information about the vehicle including task information describing at least a vehicle task that is to be offloaded to the edge device and context information about aspects relating to operation of the vehicle. The method includes triggering offloading of the vehicle task to the edge device in response to determining that at least the context information satisfies a scheduling threshold. The method includes providing, by the edge device, a result of executing the vehicle task to the vehicle.

Abstract: In an example, a method determines a reference vehicle traveling in a first lane. The first lane merges with a second lane at a merging point. The method can determine a merging plan for a merging vehicle traveling in the second lane to merge into the first lane based on a vehicle position, and in some cases the speed, of the reference vehicle in the first lane. The method can overlay, via a display device, a virtual target in a field of view of a roadway environment of a driver of the merging vehicle based on the merging plan of the merging vehicle.

Abstract: Systems, methods, and other embodiments described herein relate to determining an optimal data offloading rate for one or more connected mobile devices. In one embodiment, a method includes receiving mobile device transmission information and receiving edge server resource information. The method includes determining an offloading rate for a mobile device to an edge server based on the mobile device transmission information, the edge server resource information, and a mean-field game algorithm. The method includes outputting the offloading rate to the mobile device.

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: In accordance with one embodiment of the present disclosure, a system for generating surrounding views includes a controller. The controller may be programmed to perform operations including receiving a status information from a remote source, receiving a surround view system (SVS) request having a set of requirements from a vehicle, identifying one or more remote sources based on the status information of the one or more remote sources and the set of requirements of the SVS request, and directing the one or more remote sources to stream SVS data to the vehicle.

Abstract: System, methods, and other embodiments described herein relate to improving scheduling of computing tasks in a mobile environment for a vehicle. In one embodiment, a method includes receiving an offloading request associated with a computing task from the vehicle, wherein the offloading request includes context information and a task descriptor related to the computing task. The method also includes scheduling the computing task to execute on a server if the context information and the task descriptor satisfy criteria for using computing resources associated with the server for the vehicle. The method also includes partitioning the computing task into subtasks if the context information satisfies the criteria. A machine learning module may decide partitions of the computing task according to the context information.

Abstract: Systems, methods, and other embodiments described herein relate to determining an optimal data offloading rate for one or more connected mobile devices. In one embodiment, a method includes receiving mobile device transmission information and receiving edge server resource information. The method includes determining an offloading rate for a mobile device to an edge server based on the mobile device transmission information, the edge server resource information, and a mean-field game algorithm. The method includes outputting the offloading rate to the mobile device.

Abstract: Systems and methods for maintaining a service session in an automotive edge computing environment are disclosed herein. One embodiment computes a first estimation result pertaining to computation resources of a current edge server with which a vehicle is in communication; computes a second estimation result pertaining to link quality; computes a third estimation result pertaining to service quality; compares the first, second, and third estimation results with first, second, and third predetermined thresholds, respectively; selects a new edge server with which to maintain the service session and initiates a service migration request, when the first predetermined threshold is triggered; initiates a wireless access handover request, when only the second predetermined threshold is triggered; and selects a new edge server with which to maintain the service session and initiates a service migration request, when only the third predetermined threshold is triggered and one of two predetermined conditions is met.

Abstract: Systems and methods described herein relate to simulating edge-computing deployment in diverse terrains. One embodiment receives a layer-selection input specifying which layers among a plurality of layers in a simulation model of an edge-computing deployment are to be included in a simulation experiment; receives a set of input parameters for each of the layers specified by the layer-selection input, the set of input parameters for one of the layers specified by the layer-selection input including selection of a vehicular application whose performance in the edge-computing deployment is to be evaluated via the simulation experiment; executes the simulation experiment in accordance with the layer-selection input and the set of input parameters for each of the layers specified by the layer-selection input; and outputs, from the simulation experiment, performance data for the selected vehicular application in the edge-computing deployment.

Abstract: System, methods, and other embodiments described herein relate to selecting servers and allocating resources concurrently for offloading computing tasks from vehicles. In one embodiment, a method includes acquiring characteristics of a vehicle and a server for an offloading request, wherein the offloading request is associated with a computing task of the vehicle. The method also includes, upon satisfying criteria for optimization associated with executing the computing task remotely, determining server selection and resource allocation by processing the characteristics using modeling. The method also includes communicating the server selection and the resource allocation to the vehicle.

Abstract: A system for coordinating data offloading includes a controller. The controller is configured to at least receive a set of data metrics corresponding to a set of vehicles, generate a set of vehicle clusters based on the set of data metrics, determine a thread distribution based on the set of vehicle clusters, and transmit a data offloading control signal to each of the set of vehicles according to the thread distribution.

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 for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.