Abstract: An example method determines a plurality of traffic sections for a geographical traffic area, each traffic section including a road segment and vehicle(s) traveling on the road segment; monitors a responsive vehicle rate for a first traffic section of the plurality of traffic sections; and assigns, based on the responsive vehicle rate of the first traffic section, the first traffic section to one of a first section server dedicated to manage the first traffic section and a remote management server capable of managing the plurality of traffic sections of the geographical traffic area, the first section server comprising computing device(s) of responsive vehicle(s) in the first traffic section.

Abstract: The disclosure includes embodiments for managing a Quality of Experience (QoE) level experienced by a connected vehicle. In some embodiments, a method for the connected vehicle includes receiving a Vehicle-to-Everything (V2X) message that includes a management decision for the connected vehicle. The management decision is made based at least on a QoE status in a geographic location of the connected vehicle. The method includes modifying an operation of a vehicle control system of the connected vehicle to implement the management decision so that a QoE level experienced by the connected vehicle is improved.

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 for providing a vehicle risk evaluation and a modification for an onboard system of a vehicle to improve the operation of the onboard system. In some embodiments, a method includes generating a digital twin of a vehicle. The method includes receiving digital data recorded by a sensor and describing a condition of the vehicle as it exists in a real-world and a behavior of the vehicle as operated in the real-world. The method includes updating the digital twin of the vehicle based on the digital data so that the digital twin is consistent with the condition and the behavior.

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: 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: 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: An example method determines a plurality of traffic sections for a geographical traffic area, each traffic section including a road segment and vehicle(s) traveling on the road segment; monitors a responsive vehicle rate for a first traffic section of the plurality of traffic sections; and assigns, based on the responsive vehicle rate of the first traffic section, the first traffic section to one of a first section server dedicated to manage the first traffic section and a remote management server capable of managing the plurality of traffic sections of the geographical traffic area, the first section server comprising computing device(s) of responsive vehicle(s) in the first traffic section.

Abstract: The disclosure includes embodiments for designing and configuring network elements for a Vehicle-to-Everything (V2X) network. A method includes executing a set of simulations which is operable to generate configuration data that describes a configuration for a set of network elements that is formally verified. The set of network elements is included in the V2X network. The method includes configuring the set of network elements consistent with the configuration data.

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 adjusting a blind spot monitor on a vehicle to improve safety of the vehicle. In some embodiments, a method for the vehicle includes detecting a change in a heading of the vehicle. The method includes modifying an operation of the blind spot monitor on the vehicle based on the change in the heading of the vehicle so that performance of the blind spot monitor is enhanced to improve safety of the vehicle in a scenario when the vehicle changes the heading.

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: 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 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 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: 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.