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: 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: 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 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: 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.

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.

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: A controller for processing traffic data from vehicles is provided. The controller includes one or more processors, one or more memory modules, and machine readable instructions stored in the one or more memory modules that, when executed by the one or more processors, cause the controller to: receive raw data from a plurality of vehicles on a road; determine information about the road; select a data reduction metric among a plurality of data reduction metrics based on the information about the road; and obtain reduced data for the plurality of vehicles, the raw data being transformed to the reduced data based on the selected data reduction metric.

Abstract: The disclosure includes embodiments for providing optimum vehicle behaviors in a region. In some embodiments, a method for a connected vehicle includes transmitting, via a Vehicle-to-Everything (V2X) communication, V2X data that includes customized data describing a customized need of the connected vehicle. The method includes receiving, via the V2X communication, vehicle behavior data describing an individual optimum behavior for the connected vehicle that is determined based at least in part on the V2X data. The method includes modifying an operation of a vehicle control system of the connected vehicle based on the vehicle behavior data so that the connected vehicle implements the individual optimum behavior. An implementation of the individual optimum behavior by the connected vehicle contributes to an achievement of an overall optimum behavior of a region where the connected vehicle is located while the customized need of the connected vehicle is also satisfied.

Abstract: The disclosure includes embodiments for testing a third-party feature for compatibility and integration with an Original Equipment Manufacturer (OEM) vehicle design. A method includes modifying a vehicle design to include a third-party feature, where modifying the vehicle design generates a modified vehicle design. The method includes executing a simulation based on the modified vehicle design. The method includes extracting, from the simulation, a third-party temporal logic of the third-party feature. The method includes determining, based on the third-party temporal logic, an indication of whether the third-party feature is compliant with the vehicle design.

Abstract: In an example, a method may read, at a first timestamp using a first reader device and a second reader device of a first vehicle, a first marker position from a first marker device located on a road segment at the first marker position and a second marker position from a second marker device located on the road segment at the second marker position that is different from the first marker position. The method may determine a vehicle position of the first vehicle on the road segment at the first timestamp based on the first marker position of the first marker device and the second marker position of the second marker device.