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 event management in a vehicle by aggregating information sources. In one embodiment, a method includes selecting unfiltered events, from a stream of events that are classified and filtered, and a display medium according to a decision policy associated with an operator. The method also includes communicating the unfiltered events selected for the display medium. The method also includes adapting the decision policy according to collected tracking information of operator attention associated with the unfiltered events and reward values associated with the tracking information.

Abstract: A method includes receiving sensor data, with one or more sensor of a vehicle, wherein the sensor data is associated with an environment of the vehicle, detecting an object based on the sensor data, determining pixel coordinates of the object based on the sensor data, converting the pixel coordinates of the object to world coordinates of the object, extracting a set of features associated with the object, and transmitting the set of features and the world coordinates to a remote computing device.

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: The disclosure includes embodiments for using cross reality (XR) technologies to improve safety of a first endpoint that travels through an intersection. In some embodiments, a method for the first endpoint includes receiving first sensor data from one or more sensors of the first endpoint. The method includes determining that the first endpoint approaches an intersection based on the first sensor data. The method includes generating, based on the first sensor data, XR data that is operable to generate a graphical overlay for presenting a safe zone that the first endpoint stays within while approaching the intersection to improve safety of the first endpoint. The method includes providing the XR data to an XR viewing device to modify an operation of the XR viewing device so that the XR viewing device presents the safe zone on the graphical overlay.

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 improving event management in a vehicle by aggregating information sources. In one embodiment, a method includes selecting unfiltered events, from a stream of events that are classified and filtered, and a display medium according to a decision policy associated with an operator. The method also includes communicating the unfiltered events selected for the display medium. The method also includes adapting the decision policy according to collected tracking information of operator attention associated with the unfiltered events and reward values associated with the tracking information.

Abstract: Systems and methods described herein relate to generating a task offloading strategy for a vehicular edge-computing environment. One embodiment simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks whose data is partitioned into segments and performs, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure that includes receiving state-space information regarding the one or more vehicles and one or more intermediate network nodes; inputting the state-space information to a policy network; generating, from the policy network, an action concerning a current segment; and assigning a reward to the policy network for the action in accordance with a predetermined reward function. This embodiment produces, via the DRL training procedure, a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.

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: The disclosure includes embodiments for providing a lookup table to improve performance of a consensus mechanism used in Connected and Automated Vehicle (CAV) technologies. In some embodiments, a method for a connected vehicle includes building a lookup table that is populated with control gain values for a consensus mechanism. The control gain values are created based on a satisfaction of one or more driving constraints by the consensus mechanism. The method includes receiving data describing an initial vehicle state related to a flow of vehicles. The method includes searching the lookup table for a set of control gain values based on the initial vehicle state. The method includes implementing the consensus mechanism on the flow of vehicles based on the set of control gain values so that behavior of the flow of vehicles is coordinated.

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: A method includes receiving sensor data, with one or more sensor of a vehicle, wherein the sensor data is associated with an environment of the vehicle, detecting an object based on the sensor data, determining pixel coordinates of the object based on the sensor data, converting the pixel coordinates of the object to world coordinates of the object, extracting a set of features associated with the object, and transmitting the set of features and the world coordinates to a remote computing device.

Abstract: An example method may receive, from a requesting mobility provider (MP), request parameter(s) of a transportation request and vehicle movement requirement(s) of the requesting MP; select, from vehicle profiles of other MPs, first vehicle profile(s) of first MP(s) based on the request parameter(s) of the transportation request, the first vehicle profile(s) including a first vehicle profile representing a first vehicle of a first MP; receive, from the first MP, a mobility plan for the first vehicle to execute the transportation request, the mobility plan of the first vehicle being generated based on the request parameter(s) of the transportation request and an operational protocol of the first MP; determine that the first vehicle is qualified to execute the transportation request using the vehicle movement requirement(s) of the requesting MP and the mobility plan of the first vehicle; and provide the mobility plan of the first vehicle to the requesting MP.

Abstract: The disclosure includes embodiments that provide a digital twin-based edge server switching decision. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of digital twin data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the determining that the threshold for the predicted latency is satisfied.

Abstract: The disclosure includes embodiments including a vehicular edge server switching mechanism based on historical data and digital twin simulations. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. A method includes comparing the current driving context to a set of digital twin data and a set of historical data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the set of historical data and the determining that the threshold for the predicted latency is satisfied.

Abstract: The disclosure includes embodiments that provide a digital twin-based edge server switching decision. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of digital twin data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the determining that the threshold for the predicted latency is satisfied.

Abstract: The disclosure includes embodiments that provide a switching decision for vehicle computational offloading to a roadside edge server. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of historical data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server. The method includes causing the edge server to wirelessly provide digital data generated by the edge server responsive to a calculation. The method includes modifying an operation of the onboard vehicle computer based on the digital data.

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: The disclosure includes implementations for a connected vehicle receiving cached electronic control unit (“ECU” if singular or “ECUs” if plural) mapping solutions via a network. Some implementations of a method for a vehicle may include identifying a presence of one or more functions associated with a vehicle control system. The method may include generating a query including a mapping problem describing one or more questions about which of one or more ECUs should execute the one or more functions. The method may include providing the query to a network. The method may include receiving a response from the network. The response may include a mapping solution that describes which of the one or more ECUs should execute the one or more functions. The method may include mapping the one or more functions to the one or more ECUs as described by the mapping solution.

Abstract: The disclosure includes embodiments of a lane change timing indicator for a connected vehicle. In some embodiments, a method includes determining a time and a path for an ego vehicle to change lanes. In some embodiments, the method includes displaying, on an electronic display device of the ego vehicle, one or more graphics that depict the time and the path.