Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire detected data describing an environment of a vehicle using the at least one imaging sensor; derive reference data which describes the environment from a predefined map; compute difference data representing a difference between the detected data and the reference data; and transfer the difference data, wherein an image computed based on the difference data and the reference data represents the detected data.

Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire detected data describing an environment of a vehicle using the at least one imaging sensor; derive reference data which describes the environment from a predefined map; compute difference data representing a difference between the detected data and the reference data; and transfer the difference data, wherein an image computed based on the difference data and the reference data represents the detected data.

Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire detected data describing an environment of an autonomous vehicle using the imaging sensor; derive reference data which describes the environment from a predefined map; compute difference data representing a difference between the detected data and the reference data; and transfer the difference data, wherein an image computed based on the difference data and the reference data represents the detected data. Other embodiments are also described.

Abstract: A method includes generating a plurality of training vectors relating to different respective historical service requests generated by respective autonomous vehicles, each of the training vectors including respective features associated with the historical request to which the training vector relates. The method further includes, using the training vectors, training a model configured to generate a prediction relating to one or more future service requests, generating the prediction using the model, and outputting the generated prediction. Other embodiments are also described.

Abstract: A vehicle computing unit and method for collision avoidance. The method includes calculating a dynamic trajectory of a vehicle, wherein the dynamic trajectory of the vehicle indicates a projected movement path of the vehicle; determining whether at least one risk location is within the dynamic trajectory of the vehicle; operating the vehicle based on a driving decision selected from among a first driving decision and a second driving decision when at least one risk location is within the dynamic trajectory of the vehicle, wherein the first driving decision is determined based on inputs by an operator of the vehicle, wherein the second driving decision is determined by a vehicle computing unit of the vehicle; and operating the vehicle based on the inputs by the operator of the vehicle when no risk location is within the dynamic trajectory of the vehicle.

Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire detected data describing an environment of an autonomous vehicle using the imaging sensor; derive reference data which describes the environment from a predefined map; compute difference data representing a difference between the detected data and the reference data; and transfer the difference data, wherein an image computed based on the difference data and the reference data represents the detected data. Other embodiments are also described.

Abstract: Techniques for improving data transmission in teleoperation systems including a method for dynamic packet routing. The method includes identifying an optimal channel of a plurality of channels based on a network connectivity status of a system and historical connectivity data related to a current location of the system, wherein the system includes a plurality of network authorization devices, wherein each network authorization device is configured to enable communications via an associated channel; and routing packets to the optimal channel using a network authorization device of the plurality of network authorization devices that is associated with the optimal channel.

Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire, using the imaging sensor, detected data describing an environment of an autonomous vehicle. The processor is further configured to derive reference data, which describe the environment, from a predefined map, to compute difference data representing a difference between the detected data and the reference data, and to transfer the difference data. Other embodiments are also described.

Abstract: Techniques for improving data transmission in teleoperation systems including a method for dynamic packet routing. The method includes identifying an optimal channel of a plurality of channels based on a network connectivity status of a system and historical connectivity data related to a current location of the system, wherein the system includes a plurality of network authorization devices, wherein each network authorization device is configured to enable communications via an associated channel; and routing packets to the optimal channel using a network authorization device of the plurality of network authorization devices that is associated with the optimal channel.

Abstract: A system and method for collision warning. The method includes calculating a dynamic trajectory of a vehicle, the vehicle including at least one camera for capturing visual multimedia content, wherein the dynamic trajectory indicates a projected movement path of the vehicle, wherein the dynamic trajectory is calculated based on a respective stopping distance and a radius of movement determined for the vehicle; generating an overlay based on the at least one dynamic trajectory, wherein the overlay indicates at least one risk, wherein each of the at least one risk is located within the at least one dynamic trajectory; and applying the overlay to the visual multimedia content.

Abstract: Techniques for improving data transmission in teleoperation systems including a method for dynamic packet routing. The method includes identifying an optimal channel of a plurality of channels based on a network connectivity status of a system and historical connectivity data related to a current location of the system, wherein the system includes a plurality of network authorization devices, wherein each network authorization device is configured to enable communications via an associated channel; and routing packets to the optimal channel using the network authorization device associated with the optimal channel.

Abstract: Techniques for improving data transmission in teleoperation systems including a method for dynamic packet routing. The method includes identifying an optimal channel of a plurality of channels based on a network connectivity status of a system and historical connectivity data related to a current location of the system, wherein the system includes a plurality of network authorization devices, wherein each network authorization device is configured to enable communications via an associated channel; and routing packets to the optimal channel using the network authorization device associated with the optimal channel.

Abstract: A system and method for collision warning. The method includes calculating a dynamic trajectory of a vehicle, the vehicle including at least one camera for capturing visual multimedia content, wherein the dynamic trajectory indicates a projected movement path of the vehicle, wherein the dynamic trajectory is calculated based on a respective stopping distance and a radius of movement determined for the vehicle; generating an overlay based on the at least one dynamic trajectory, wherein the overlay indicates at least one risk, wherein each of the at least one risk is located within the at least one dynamic trajectory; and applying the overlay to the visual multimedia content.

Abstract: A vehicle computing unit and method for collision avoidance. The method includes calculating a dynamic trajectory of a vehicle, wherein the dynamic trajectory of the vehicle indicates a projected movement path of the vehicle; determining whether at least one risk location is within the dynamic trajectory of the vehicle; operating the vehicle based on a driving decision selected from among a first driving decision and a second driving decision when at least one risk location is within the dynamic trajectory of the vehicle, wherein the first driving decision is determined based on inputs by an operator of the vehicle, wherein the second driving decision is determined by a vehicle computing unit of the vehicle; and operating the vehicle based on the inputs by the operator of the vehicle when no risk location is within the dynamic trajectory of the vehicle.

Abstract: A method includes generating a plurality of training vectors relating to different respective historical service requests generated by respective autonomous vehicles, each of the training vectors including respective features associated with the historical request to which the training vector relates. The method further includes, using the training vectors, training a model configured to generate a prediction relating to one or more future service requests, generating the prediction using the model, and outputting the generated prediction. Other embodiments are also described.

Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire, using the imaging sensor, detected data describing an environment of an autonomous vehicle. The processor is further configured to derive reference data, which describe the environment, from a predefined map, to compute difference data representing a difference between the detected data and the reference data, and to transfer the difference data. Other embodiments are also described.

Abstract: According to examples, an apparatus for managing alerts pertaining to additions of users to a user group in a computer network may include a processor and a memory, which may have stored thereon machine readable instructions that are to cause the processor to, during a learning period, identify an entity that added a user to the user group during the learning period and enter an identification of the identified entity into an allowed entity list for the user group. Following the learning period, the instructions are to cause the processor to identify a user addition event that indicates that an adding entity added another user to the user group, determine whether the adding entity is in the allowed entity list, and manage issuance of an alert regarding the user addition event based upon whether the adding entity is in the allowed entity list to reduce a number of issued alerts.

Abstract: According to examples, an apparatus for managing alerts pertaining to additions of users to a user group in a computer network may include a processor and a memory, which may have stored thereon machine readable instructions that are to cause the processor to, during a learning period, identify an entity that added a user to the user group during the learning period and enter an identification of the identified entity into an allowed entity list for the user group. Following the learning period, the instructions are to cause the processor to identify a user addition event that indicates that an adding entity added another user to the user group, determine whether the adding entity is in the allowed entity list, and manage issuance of an alert regarding the user addition event based upon whether the adding entity is in the allowed entity list to reduce a number of issued alerts.